Parameters of each applied filters and tools.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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Even if noise is one of the oldest pollutants referred to in history, for years, the problem of noise pollution has been often considered less important than others related to the environment, such as air pollution, water pollution, and waste management.The regulations in force to contain the noise have become increasingly stringent as each individual is constantly exposed to noise and often the noise is treated just as a scourge of modern society. Making noise is becoming easier and cheaper each day, but just the opposite for controlling it. Deeper studies are needed to understand the core of current noise problems; new materials and techniques are needed to control them. This book is a combination of theory and practice based on the latest research. The studies in this book range from evaluation methods for the perception of noise and outline forecast criteria that can be integrated with applications for acoustic mapping as well as the use of innovative techniques and materials for its abatement. The main purpose of this book, organized in 8 chapters, is to provide an overview of the recent studies in this field and the applications in different research studies. The authors, contributing to the success of this book, provide a series of practical applications of their recent studies aimed at the reduction of noise in different environments. The editors would like to thank all the authors who, through their studies and research, have accepted our invitation to share recent discoveries in this field with the scientific community.",isbn:"978-1-83968-240-7",printIsbn:"978-1-83968-239-1",pdfIsbn:"978-1-83968-241-4",doi:"10.5772/intechopen.87872",price:119,priceEur:129,priceUsd:155,slug:"noise-and-environment",numberOfPages:172,isOpenForSubmission:!1,isInWos:null,hash:"11e8fca2f0f623d87dfbc3cf2b185e0d",bookSignature:"Daniela Siano and Alice Elizabeth González",publishedDate:"February 3rd 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10042.jpg",numberOfDownloads:1268,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:2,hasAltmetrics:0,numberOfTotalCitations:3,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 19th 2019",dateEndSecondStepPublish:"February 28th 2020",dateEndThirdStepPublish:"April 28th 2020",dateEndFourthStepPublish:"July 17th 2020",dateEndFifthStepPublish:"September 15th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"9960",title:"Dr.",name:"Daniela",middleName:null,surname:"Siano",slug:"daniela-siano",fullName:"Daniela Siano",profilePictureURL:"https://mts.intechopen.com/storage/users/9960/images/system/9960.jpg",biography:'Daniela Siano was born in Naples - Italy, and graduated in Aeronautical Engineering from the University of Naples “Federico II”, Italy in 1994. Until 2001, she was a researcher in the Acoustic and Vibration Department at C.I.R.A. (Italian Aerospace Research Center). From 2001 until now, she has been a researcher at the National Research Council of Italy (CNR) in the field of acoustic and vibration in the transport field. She is responsible for the Acoustic and Vibration Laboratory. She is qualified to University Associate Professor – Sector Machines and Systems for Energy and Environment, and is also qualified in the Engineer Profession. She had a scholarship granted by M.A.R.S.- Microgravity Advanced Research and Support - Dornier GmbH (Friedrichshafen - Germany), Euromaster in \\"Total Quality Management\\" at DIMP – Department of Mechanical and Production Engineering – University of Naples Federico II – Italy, as well as assistant in “Fluid Machines I”, “Fluid Machines II”, and “Applied Mechanics”. She is the tutor of more than 30 Master\\\'s students and 7 Ph.D. students. 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\r\n\tPomegranate (Punica granatum L.) is one of the oldest edible fruits in the Meditterranean area and has been used extensively in the folk medicine. Popularity of pomegranate has increased especially in the last decade because of the health effects (i.e. anti-oxidant, anti-microbial, anti-cancer,, anti-mutagenic e.t.c.) of the fruit. Polyphenols, represent the predominant class of phytochemicals of pomegranate fruits, mainly consisting of hydrolysable tannins, gallotannins, ellagitannins and ellagic acid. Pomegranate fruit is composed of three parts: the seeds, the arils and the peels.
\r\n\r\n\tThe aim of this book is to present a) the chemistry of bioactive compounds in the different parts of pomegranate fruit with an extensive description of their bioavailability and health promoting metabolites b) The health benefits including the current pro-clinical and clinical studies c) The recent advances in food processing technology d) A market overview and future perspectives in pomegranate industrial sectors.
",isbn:"978-1-83968-464-7",printIsbn:"978-1-83968-447-0",pdfIsbn:"978-1-83968-465-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"32485dda924a1e5b95c3d8ec61e6a918",bookSignature:"Dr. Vasiliki Lagouri",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10171.jpg",keywords:"Polyphenolic Compounds, Ellagitannins, Metabolites, Cardiovascular Disease, Cancer, Inflammation, Green Technology, Fermentation, Encapsulation, Food Industry, Cosmetics Industry, Pharmaceutical Industry",numberOfDownloads:24,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 24th 2020",dateEndSecondStepPublish:"September 21st 2020",dateEndThirdStepPublish:"November 20th 2020",dateEndFourthStepPublish:"February 8th 2021",dateEndFifthStepPublish:"April 9th 2021",remainingDaysToSecondStep:"5 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Lagouri has over 30 publications in international journals, conference proceedings, and 3 book authorships and editorships in food chemistry, natural antioxidants, and olive oil and olives as functional foods (number of citations more than 400).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"232589",title:"Dr.",name:"Vasiliki",middleName:null,surname:"Lagouri",slug:"vasiliki-lagouri",fullName:"Vasiliki Lagouri",profilePictureURL:"https://mts.intechopen.com/storage/users/232589/images/system/232589.jpeg",biography:"Vasiliki Lagouri BA MSc PhD received her three degrees from Aristotle University of Thessaloniki and National and Kapodistrian University of Athens, Greece. She has research and academic experience (1992-2019) at the Chemistry Department of Aristotle University of Thessaloniki, Food Technology Department in Technological Educational Institute of Thessaloniki, Post doc positions in the Department of Organic Chemistry, Faculty of Chemistry and Department of Pharmacognosy and Chemistry of Natural Products, Faculty of Pharmacy, School of Health Sciences at National and Kapodistrian University of Athens. Her current position is Project Manager in National Hellenic Research Foundation (NHRF), Institute of Chemical Biology (ICB). \r\nShe has over 30 of publications in International Journals, Conference Proceedings and 3 Book authorships and editorships in food chemistry, natural antioxidants, and olive oil and olives as functional foods (number of citations more than 400). She has research experience on experimental designs and applications of different methods to study the chemistry of natural sources, the isolation, identification and quantification of biologically active polar and non-polar compounds. She offered her services as a reviewer for the Journals: International Journal of Food properties, Journal of the Science of Food and Agriculture, Central European Journal of Chemistry, Separation Science and Technology, Natural Products Research, Nutrients, Molecules. She is a member of the American Chemical Society, Society Free-Radical Research-Europe (SFRR-E), Oxygen Club of California (OCC), ISEKI-Food Association: European Association for Integrating Food Science and Engineering Knowledge Into the Food Chain and the Greek Chemist’s Union and she is included in Who’s Who of America.",institutionString:"National Hellenic Research Foundation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"National Hellenic Research Foundation",institutionURL:null,country:{name:"Greece"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:[{id:"75059",title:"Antimicrobial Potential of Pomegranate Extracts",slug:"antimicrobial-potential-of-pomegranate-extracts",totalDownloads:24,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"259492",firstName:"Sara",lastName:"Gojević-Zrnić",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/259492/images/7469_n.png",email:"sara.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7183",title:"Functional Foods",subtitle:null,isOpenForSubmission:!1,hash:"8023d990ea5254d039f9c438b66899c6",slug:"functional-foods",bookSignature:"Vasiliki Lagouri",coverURL:"https://cdn.intechopen.com/books/images_new/7183.jpg",editedByType:"Edited by",editors:[{id:"232589",title:"Dr.",name:"Vasiliki",surname:"Lagouri",slug:"vasiliki-lagouri",fullName:"Vasiliki Lagouri"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and Environment",subtitle:null,isOpenForSubmission:!1,hash:"9005c36534a5dc065577a011aea13d4d",slug:"hyperspectral-imaging-in-agriculture-food-and-environment",bookSignature:"Alejandro Isabel Luna Maldonado, Humberto Rodríguez Fuentes and Juan Antonio Vidales Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/6418.jpg",editedByType:"Edited by",editors:[{id:"105774",title:"Prof.",name:"Alejandro Isabel",surname:"Luna Maldonado",slug:"alejandro-isabel-luna-maldonado",fullName:"Alejandro Isabel Luna Maldonado"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69899",title:"Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study of a Spine Surgery",doi:"10.5772/intechopen.89950",slug:"surgical-planning-and-additive-manufacturing-of-an-anatomical-model-a-case-study-of-a-spine-surgery",body:'Scoliosis is a three-dimensional (3D) deformity in the natural shape of spine that requires surgery in serious cases [1]. A lateral deviation of the spine greater than 10 degrees is accepted as scoliosis and an abnormal sideways curvature is observed from the frontal plane of a patient, while a healthy spine should look like straight [2, 3]. Although hereditary factors play a role in the etiology, most of the cases (80% of the patients) with unknown cause are called idiopathic scoliosis and classified into three main groups based on the age, namely, (i) infantile (up to 3 years old), (ii) juvenile (4–9 years old), and (iii) adolescent (10 years old to teen years) [4]. The severity of scoliosis is also determined by Cobb method that provides obtain information about the curvature of the spine in terms of degrees. According to these measurements, a scoliotic curve of the spine is defined as (i) mild scoliosis (10–25°, requires monitoring), (ii) significant scoliosis (25–40°, treated with bracing), and (iii) severe scoliosis (over 40°, requires surgery) [5]. Recently, a number of the scoliosis patients are growing up to 2–3% worldwide due to the unawareness of people [6]. It is important to predict risk of progression, diagnose at early stage for preventing degenerative effects, and contribute to the patient’s quality of life. Unfortunately, in some cases, rapid progress on spine deformity or late diagnosis is resulted with surgical operations that are necessary to stabilize the spine by means of rod placement on each affected vertebra [6]. All operations require precise processes in a limited workspace because of the spinal nerves and blood vessels. Besides, an injury of a vessel around the surgical site may initiate clotting that may result with pulmonary embolism or even death [7]. Therefore, novel technologies and methods are of great importance to assist or plan the surgery in advance.
Medical imaging technologies such as multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) are rapidly evolving to visualize more complex tissues of the human body [8]. Furthermore, high-resolution images provide radiologists more accurate diagnosis [9]. Although 3D visualization of the scanned tissues simplifies the surgical procedures, patients may undergo a suboptimal outcome after the treatment [10]. On the other hand, two-dimensional (2D) computer screens prevent the direct interaction with the target 3D model [11]. While virtual presurgical planning offers more precise results and overcomes mentioned drawbacks above, it is not applicable for every hospital because of its high costs [10]. Additive manufacturing, layer-by-layer fabrication of a physical object, is increasingly being used especially in the fields of medicine. Nowadays, these technologies are utilized with medical imaging to produce patient-specific (i) medical devices, orthosis, and prosthesis [12], (ii) anatomical to assist-surgical models [13], (iii) body parts [14], (iv) dental and maxillofacial implants [15], (v) blood vessels [16], and (vi) organs [17]. Typical 2D digital imaging and communications in medicine (DICOM) files from the CT or MRI images are transformed into a 3D standard tessellation language (STL) file to perform 3D printing of the target model. A wide range of polymers including glass, ceramic, metal, or biological materials may be used to create complex models in a cost-effective way compared to conventional manufacturing methods [18]. Although the latest innovative studies are focused on a functional tissue or even an organ printing, clinical bedside applications of these technologies are difficult and limited [11]. On the other hand, surgical planning or patient-specific implant production via 3D printing is available for any case and widely used to manage a complex surgery or achieve the exact placement of an implant [18]. Consequently, success rate of each surgery is significantly increased as well as it becomes minimally invasive and requires shorter recovery owing to the preoperative review [19, 20].
Surgical planning by means of additive manufacturing consists of five basic steps, namely, (i) data acquisition, (ii) image segmentation, (iii) 3D model reconstruction, (iv) 3D printing, and (v) preoperative review [21]. In data acquisition step, a medical imaging system such as CT is used to obtain 2D DICOM images in general. The accuracy of surgical planning is first dependent on the contrast-to-noise ratio (CNR) of scanned raw images [22]. High CNR values provide detailed segmentation before the 3D reconstruction of a model. These images are then processed via a commercial (Materialize Mimics) or an open-source (InVesalius) medical image processing software using the default segmentation functions or manual selection of the region of interest on each image during the segmentation step. Thus, the target anatomical model’s contour is masked on each image section. In 3D model reconstruction step, the mask images are positioned sequentially to form a solid model in 3D workspace of the software. Tissue-specific filters or data sets may be applied to minimize noise using predefined thresholds before and after the 3D reconstruction process. The solid model is exported as a STL file and prepared to be 3D printed. In 3D printing step, the STL file is 3D printed using one of the additive manufacturing methods such as fused deposition modeling (FDM), selective laser sintering (SLS), or stereolithography (SLA). In a preoperative review step, the printed model is evaluated and used before and during the surgery (requires sterilization) as a reference model that guide the operation as well as it can be used for custom implant or assistive surgical apparatus creation such as drill guides [23]. Since surgical planning is important and plays a crucial role during the surgery, reference 3D model requires to be created with minimal geometrical errors. The accuracy of 3D reconstructed models can be analyzed by calculating the Hausdorff distance (HD) and dice similarity coefficient (DSC) values for each model [24, 25]. While DSC measures the volume overlap between two models that ranges from 0 (no overlap) to 1 (exact overlap), HD calculates the Euclidean distance between each binary volume [26, 27]. Furthermore, several methods (geometric distance, roughness, structure, saliency, and strain-energy-based calculations) may be implemented to assess visual quality and accuracy of the 3D models [28]. In this study, a spine model of a scoliosis patient is first acquired and then 3D reconstructed in MIMICS software (manual and automatic segmentation) using different filters and parameters to compare each geometrical error. The results of the DSC and HD values of each reconstructed model reveal the ideal protocol for surgical planning of a spine surgery and determine the accuracy of the created model.
A 6-year-old patient (male) who has a congenital scoliosis history participated to this study for obtaining raw image data. Medical scanning process was performed at the Radiology Department, Faculty of Medicine Kocaeli University, using a CT scanner (Aquillion 64, Toshiba Medical Systems, Tokyo, Japan). Ethical permission was received by the Ethics and Research Committee of Kocaeli University (reference number, KU GOKAEK 2019/204). A full spinal CT scanning was performed at 2 mm of slice thickness, 140 mm field of view (FoV) and 135 kV (40 mA, 1 s). Raw DICOM images (449 slices) were obtained from the picture archiving and communication system (PACS) server of the Radiology Department (Figure 1(a)). Thus, data acquisition process, the first step of the surgical planning, was completed.
3D Surgical model segmentation of the reference model (a) An image of the 3D raw model, based on non-filtered DICOM images in Sectra workspace, a 3D DICOM viewer of the default system software that does not allow 3D model exporting or processing, (b) Importing non-filtered DICOM images in MIMICS, (c) Segmentation of each image and obtaining the exact contour of target model in a maximum allowable noisy form in MIMICS, (d) 3D reconstruction of the raw DICOM images in a noisy form via MIMICS, (e) Manual noise cleaning in 3-Matic, (f) Manual surface reconstruction and fixing the geometrical errors of 3D raw model in 3-Matic, (g) Fixing the sharp cornered bonded surfaces in Meshmixer.
MIMICS software (v19) was utilized in image segmentation step, and a reference model was automatically reconstructed via MIMICS without any filters (Figure 1(b)). DICOM files (449 images) were imported, and segmentation process was performed using the thresholding tool at predefined threshold set of the bone (226, Hounsfield Unit, HU as lower threshold, and 3071 HU as higher threshold). These parameters were ideal to obtain exact contour of the target model in a maximum allowable noisy form according to the raw DICOM images for this case. Segmentation step was completed after each image section was masked and highlighted by a different color (Figure 1(c)).
3D calculation function was utilized at high quality to reconstruct the 3D raw surface, and a quite noisy model was created with desired contour based on the masked images in 3D model reconstruction step (Figure 1(d)). Each image was positioned sequentially to form a 3D raw model of the spine. Since there was noise in the workspace and model’s surface, the 3D raw model was directly exported to 3-matic software (v11, Materialize) for manual surface reconstruction and noise elimination (Figure 1(e)). The surface geometry of the model was manually revised, and the noise was manually cleared via 3-matic software according to the raw image geometries in MIMICS. Polygon area mark tool under the Mark—Area Mark menu was used to select each noisy surface and subsequently deleted. The gaps of the deleted surfaces were marked as bad contours on the 3D raw model. The bad contours were then selected and fixed, respectively, using the Fill Hole Freeform tool under the Fix menu. The Fill Hole Freeform process was performed at medium triangulation quality and created in tangent form. After manual surface reconstruction, the Fix Wizard tool under the Fix menu was utilized to fix geometrical normals, stitching, noise shells, holes, triangles, overlaps, and shells on the model. The errors were automatically fixed by clicking Follow Advice, Apply, and Update buttons, respectively, for each option (Figure 1(f)). Finally, the patient ID was added using the Quick Label tool under the Finish menu. Thus, 3D-reconstructed reference surgical model was obtained and exported as STL file by clicking File, Export, and STL buttons, respectively. The reference model was exported at binary format and one scaling factor. Some sharp cornered bonded surfaces that occurred by Fix Wizard tool during the fixing process were also revised in Meshmixer (v3.4.35, Autodesk) manually to obtain exact geometry of target anatomical model. Robust Smooth tool under the Sculp – Brushes menu was used (brush parameters, 10 strength, 10 size, 0 depth, and 0 laziness) to fix sharp surfaces (Figure 1(g)). The 3D model reconstruction step was completed after the reference model has taken its final shape (48 hours of work). 3D reconstruction of the reference surgical model is illustrated step by step in Figure 1.
Test models were separated into three groups, namely, (i) preprocessed, (ii) post-processed, and (iii) fully processed models. Preprocessed models were obtained using the filtered DICOM images before the segmentation step without any post-process in MIMICS. Post-processed models were created from the raw DICOM images and only processed via 3-matic after the model was reconstructed (Figure 2(a)). Fully processed models were formed with a combination of both processes. Five different image filters of MIMICS, (i) binomial blur (Figure 2(b)), (ii) curvature flow (Figure 2(c)), (iii) discrete Gaussian (Figure 2(d)), (iv) mean (Figure 2(e)), and (v) median (Figure 2(f)), were applied on the raw DICOM images, respectively, and then the segmentation step was performed separately for each sample on preprocessed models. Two different 3D tools of 3-matic, (i) Smooth (Figure 2(g)) and (ii) wrap (Figure 2(h)), were utilized separately for each sample in post-processing case. Preprocessing, post-processing, and full-processing parameters of the 3D reconstructed surgical models are given in Table 1.
Reference model and each generated 3D test model with a local mesh view (a) Reference model, (b) Binomial blur filter in MIMICS, (c) Curvature flow filter in MIMICS, (d) Discrete gaussian filter in MIMICS, (e) Mean filter in MIMICS, (f) Median filter in MIMICS, (g) Smooth function in 3-Matic and (h) Wrap function in 3-Matic.
Pre-processing parameters of applied filters in MIMICS | |||
---|---|---|---|
Model no. | Filter name | Parameter | Value |
1 | Binomial blur | Number of iterations | 5 |
2 | Curvature flow | Time step | 0.5 |
Number of iterations | 5 | ||
3 | Discrete Gaussian | Gaussian variance | 5 |
Max kernel width | 5 | ||
4 | *Mean | Filter radius | 3 |
5 | Median | Filter radius | 3 |
Post-processing parameters of applied tools in 3-matic | |||
6 | *Smooth | Smooth factor | 1 |
7 | Wrap | Gap closing | 0.05 |
Smallest detail | 0.5 | ||
Full-processing parameters of both applied filter and tool | |||
8 | *Mean with *Smooth together | Filter radius | 3 |
Smooth factor | 0.5 |
Parameters of each applied filters and tools.
(* = best similarity results).
Before the calculation of HD and DSC values by CloudCompare software, each spine model was processed via 3-matic software that was necessary to determine the spine regions equally on each sample model and obtain more correct results in accuracy assessment. Therefore, the spine sections of each model were extracted manually and exported using the default options. All test models were imported into the CloudCompare software (v2.11, Open Source) and then compared with the reference model in terms of the HD and DSC results [29]. The best results of the preprocessing and the post-processing cases were combined and applied together to form the fully processed model. Finally, the fully processed model was compared to the reference model to reveal the ideal solution for 3D surgical model reconstruction. Each generated test model is illustrated in Figure 2.
HD of the test models was calculated by means of importing and analyzing the 3D models, the reference model and one of the test models at the same time, in CloudCompare workspace. Both models were then aligned using Registration Match bounding function—under the Tools menu. This process is required to align the box centers (volume frames) of models before performing any similarity function in order to obtain accurate results. Fine registration function under the Tools—Registration menu was applied on each model to highlight the difference of point clouds without any scale adjustment. The fine registration function also applies a rotation to compared model (generates a rotation matrix with a theoretical overlap value) to provide an ideal Overlap in volumes of the both reference and compared test model. Finally, HD value (mean distance) was computed via Distances—Cloud/Mesh Dist under the Tools menu. DSC values were calculated similarly via importing the reference model and a test model into the 3D workspace of CloudCompare software. The match bounding and fine registration steps were also performed to obtain perfect alignment. After the alignment process, a plugin named as Cork, under the Plugins menu, was utilized to obtain the intersection of both models in terms of volume (cube units). Each volume was also measured by means of the mesh measure volume function under the Edit menu. The DSC value of each model was then calculated to obtain overlapped volume between the two models according to the formula given below (α = total volume of the reference model, β = total volume of the compared model, and α ∩ β = intersection of both models in terms of volume):
Wrap tool, binomial blur, and curvature flow filters produce high geometrical errors, while mean filter produces the lowest geometrical error. Furthermore, HD and DSC results of the curvature flow and discrete Gaussian filters are close to mean filter. Smooth tool and mean filter produce almost the same volume of the reference model. However, binomial blur filter and wrap tool generate unacceptably different volumes. Moreover, the DSC results of the both mentioned functions are not overlapping properly. Each result is illustrated in Table 2.
HD and DSC results of each test model | ||||||
---|---|---|---|---|---|---|
No. | Filter-tool name | Filter type | Model volume (cube units) | Max error (%) | HD (mm) | DSC (0–1) |
1 | Binomial blur | Pre | 80393.9 | 9.502507 | 1.083704 | 0.547654 |
2 | Curvature flow | Pre | 81445.2 | 8.31908 | 1.107846 | 0.923381 |
3 | Discrete Gaussian | Pre | 81787.5 | 7.933765 | 1.034391 | 0.911621 |
4 | Mean | Pre | 82308.1 | 7.347738 | 1.041506 | 0.913479 |
5 | Median | Pre | 79867.7 | 10.09483 | 1.043368 | 0.911775 |
6 | Smooth | Post | 90480.1 | −1.851286 | 1.185564 | 0.937718 |
7 | Wrap | Post | 113,092 | −27.30496 | 0.461971 | 0.871986 |
8 | Mean + Smooth | Together | 82231.4 | 7.434077 | 1.041154 | 0.916657 |
Reference model volume (cube units): 88835.5 |
Results of each applied filters and tools (DSC value: 0 = no overlap, 1 = exact overlap, negative max error values indicate a larger volume than the reference model while positive values mean a lower volume according to the reference model in terms of cube units).
Smooth and wrap tools generate undesired mesh structures caused by the noise during the segmentation step in MIMICS. HD results of each test model are illustrated in Figure 3.
The intersection of reference model and each generated 3D test model with a local view (a) Reference model and Binomial blur pre-filtered test model, (b) Reference model and Curvature flow pre-filtered test model, (c) Reference model and Discrete gaussian pre-filtered test model, (d) Reference model and Mean pre-filtered test model, (e) Reference model and Median pre-filtered test model,( f) Reference model and Smooth post-filtered test model and (g) Reference model and Wrap post-filtered test model.
The results of the last case given in Table 2 (mean filter with Smooth tool together) show that increased Smooth provides to obtain better HD and DSC results when compared to the fourth test case (mean filter only). However, the max error is proportionally increased with the Smooth tool. Reference model, mean filter, and Smooth tool results are illustrated in Figure 4.
Results of each produced model. (a) Reference model, (b) Mean filter and (c) Smooth tool.
According to the results, binomial blur filter, median filter, and wrap tool are not suitable for surgical planning of a spine model due to the high error percentage in volume. These functions produce misleading information when used alone and may be used with another filter together at lower parametric values. Although Smooth tool provides better DSC result than the curvature flow filter, mesh surface improvement should not be performed by means of this tool only because of the undesired mesh structures caused by the noise. Therefore, a prefilter-like curvature flow, discrete Gaussian, or mean should to be applied on raw DICOM images to filter noise more accurately during the segmentation step, and Smooth tool should to be used at lower values after 3D model reconstruction process. It should be noted that some geometrical errors may not be fixed via 3-matic software and may require a third party software such as Geomagic Design X or Autodesk Meshmixer. On the other hand, fractured or unbounded bone structures may be lost during the noise filtering process. Therefore, some functions such as largest surface selection in MIMICS should not be used in complex cases, and target model should be cleared manually after the 3D reconstruction process. Mesh distribution of the models require to be uniform to perform a healthy DSC calculation. Both reference and compared test models may be processed via optimize mesh tool in Geomagic Design X to achieve uniform mesh distribution on each model.
While software optimizations may be performed to obtain an ideal protocol, improvements may also be applied on used CT device at the beginning for more correct presurgical planning. Devices with more advanced detector structure, which could provide to operate at lower voxels and have high beam quality, may reduce noise on raw data images. For example, it is known that dual energy computed tomography (DECT) is more suitable for tissue segmentation than single-energy computed tomography (SECT) [30]. Besides, some known sources of noise (beam hardening, partial volume effect, etc.) can be reduced by making some adjustments before imaging such as in monochromatic X-ray, reduced beam hardening effect and thin section thickness, and reduced the partial volume effect, which are very important for spine imaging [31, 32]. Additionally, using higher tube current provides sharper image because of the amount of beam delivery to the target tissue. However, high current may not be applied in all cases because of the increased radiation dose given to the patient.
In this study, an ideal protocol for surgical planning of a spine surgery is defined with measurable accuracy. Thus, success rate of a spine surgery may be increased especially for the severe cases owing to the more accurate preoperative review.
We declare that we have no acknowledgments.
Financial institutions need to carefully manage financial losses. For example, the claims made against short-term insurance policies need to be analysed in order to enable an insurance company to determine the reserves needed to meet their obligations and to determine the adequacy of their pricing strategies. Similarly, banks are required in terms of regulation to set aside risk capital to absorb unexpected losses that may occur. Of course, financial institutions are more interested in the total amount of claims or the aggregate loss occurring over one year in the future, than the individual claims or losses. For this reason, their focus will be on what may happen in the year ahead rather than what has happened in the past. Popular modelling methods involve the construction of annual aggregate claim or loss distributions using the so-called loss distribution approach (LDA) or random sums method. Such a distribution is assumed to be an adequate reflection of the past but need to be forward looking in the sense that anticipated future losses are taken into account. The constructed distribution may then be used to answer questions like ‘What aggregate loss level will be exceeded only once in c years?’ or ‘What is the expected annual aggregate loss level?’ or ‘If we want to guard ourselves against a one in a thousand-year aggregate loss, how much capital should we hold next year?’ The aggregate loss distribution and its quantiles will provide answers to these questions and it is therefore paramount that this distribution is modelled and estimated as accurately as possible. Often it is the extreme quantiles of this distribution that is of interest.
\nUnder Basel II’s advanced measurement approach, banks may use their own internal models to calculate their operational risk capital, and the LDA is known to be a popular method for this. A bank must be able to demonstrate that their approach captures potentially severe ‘tail’ events and they must hold capital to protect them against a one-in-a-thousand-year aggregate loss. To determine this capital amount, the 99.9% Value-at-Risk (VaR) of the aggregate distribution is calculated [1]. In order to estimate a one-in-a-thousand-year loss, one would hope that at least a thousand years of historical data is available. However, in reality only between five and ten years of internal data is available and scenario assessments by experts are often used to augment the historical data and to provide a forward-looking view.
\nThe much anticipated implementation of Basel III will require banks to calculate operational risk capital on a new standardised approach, which is simple, risk-sensitive and comparable between different banks [2]. Although the more sophisticated internal models described above will no longer be allowed in determining minimum regulatory capital, these models will remain relevant for the determination of economic capital and decision making within banks and other financial institutions. It is also suggested that LDA models would form an integral part of the supervisory review of a bank’s internal operational risk management process [3]. For this reason, we believe the LDA remains relevant and will continue to be studied and improved on.
\nIn this chapter we provide an exposition of statistical methods that may be used to estimate VaR using historical data in combination with quantile assessments by experts. The proposed approach has been discussed and studied elsewhere (see [4]), but specifically in the context of operational risk and economic capital estimation. In this chapter we concentrate on the estimation of the VaR of the aggregate loss or claims distribution and strive to make the approach more accessible to a wider audience. Also, based on the implementation done for major banks, we include some practical guidelines for the use and implementation of the method in practice. In the next section we discuss two approaches, Monte Carlo and Single Loss Approximation, that may be used for the approximation of VaR assuming known distributions and parameters. Then, in the third section (Historical data and scenario modelling), we will discuss the available sources of data and formulate the scenario approach and how these may be created and assessed by experts. This is followed, in section four (Estimating VaR), by the estimation of VaR using three modelling approaches. In the fifth section (Implementation recommendations) some guidelines on the implementation of the preferred approach are given. Some concluding remarks are made in the last section.
\nLet the random variable \n
The compound Poisson distribution \n
\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|
1.00 | \n5.00 | \n0.60 | \n0.33 | \n
1.00 | \n2.00 | \n0.60 | \n0.83 | \n
1.00 | \n1.00 | \n1.00 | \n1.00 | \n
1.00 | \n1.50 | \n0.50 | \n1.33 | \n
1.00 | \n0.30 | \n1.80 | \n1.85 | \n
1.00 | \n0.17 | \n2.50 | \n2.35 | \n
Parameter sets of Burr distribution.
Variation obtained in the VaR estimates for different values of EVI and frequency.
In principle, infinitely many repetitions are required to get the exact true VaR. The large number of simulation repetitions involved in the MC approaches above motivates the use of other numerical methods such as Panjer recursion, methods based on fast Fourier transforms [5] and the single loss approximation (SLA) method (see e.g. [6]). For a detailed comparison of numerical approximation methods, the interested reader is referred to [7]. The SLA has become very popular in the financial industry due to its simplicity and can be stated as follows: If \n
states that the \n
Let \n
i.e. \n
Note that we then have the identity
\nThis identity represents \n
It is practice in operational risk management to use different data sources for modelling future losses. Banks have been collecting their own data, but realistically, most banks only have between five and ten years of reliable loss data. To address this shortcoming, loss data from external sources and scenario data can be used by banks in addition to their own internal loss data and controls [12]. Certain external loss databases exist, including publicly available data, insurance data and consortium data. The process of incorporating data from external sources requires due consideration because of biases in the external data. One method of combining operational losses collected from various banks of different sizes and loss reporting thresholds, is discussed in [13]. In the remainder of our discussion we will only refer to historical data, which may be a combination of internal and external loss data.
\nThree types of scenario assessments are also suggested to improve the estimation of the severity distribution, namely the individual scenario approach, the interval approach, and the percentile approach. In the remainder of the chapter we discuss the percentile approach as we believe it is the most practical of the existing approaches available in the literature [4]. That being said, it should be noted that probability assessments by experts are notoriously difficult and unreliable as discussed in [14]. We mentioned previously that it is often an extreme quantile of the aggregate loss distribution that is of interest. In the case of operational risk, the regulator requires that the one-in-a-thousand-year quantile of this distribution be estimated, in other words the aggregate loss level that will be exceeded once in a thousand years. Considering that banks’ only have limited historical data available, i.e. maximum of ten years of internal data, the estimation of such a quantile, using historical data only, is a near impossible task. So modellers have suggested the use of scenarios and experts’ assessments thereof.
\nWe advocate the use of the so-called 1-in-\n
If the annual loss frequency is \n
To see this, let \n
As illustration of the complexity of the experts’ task, take \n
Returning to the SLA i.e. \n
In the light of the above arguments one has to take in consideration: (a) the SLA gives only an approximation to the VaR we are trying to estimate, and (b) experts are very unlikely to have the experience or the information at their disposal to assess a 1-in-1000 year event reliably. One can realistically only expect them to assess events occurring more frequently such as once in 30 years.
\nReturning to the oracle’s answer in (4), the expert has to consider both the true severity distribution and the annual frequency when an assessment is provided. In order to simplify the task of the expert, consider the mixed model in (3) discussed in the previous section. This model will assist us in formulating an easier question for the expert to answer. Note that the oracle’s answer to the question in the previous setting can be stated as \n
Suppose we have available \n
Assume that we have available only historical data and that we collected the loss severities of a total of \n
\n
Generate \n
Generate \n
Repeat i and ii \n
The estimation of VaR using the above-mentioned naïve approach has been discussed in several books and papers (see e.g. [11]). [16] stated that heavy-tailed data sets are hard to model and require much caution when interpreting the resulting VaR estimates. For example, a single extreme loss can cause drastic changes in the estimate of the means and variances of severity distributions even if a large amount of loss data is available. Annual aggregate losses will typically be driven by the value of the most extreme losses and the high quantiles of the aggregate annual loss distribution are primarily determined by the high quantiles of the severity distributions containing the extreme losses. Two different severity distributions for modelling the individual losses may both fit the data well in terms of goodness-of-fit statistics yet may provide capital estimates which may differ by billions. Certain deficiencies of the naïve estimation approach, in particular, the estimation of the severity distribution and the subsequent estimation of an extreme VaR of the aggregate loss distribution, are highlighted in [15].
\nIn Figure 2 below we used the naïve approach to illustrate the effect of some of the above-mentioned claims. In Figure 2(a) we assumed a Burr distribution, i.e. T_Burr(1, 0.6, 2), as our true underlying severity distribution. In the top panel we show the distribution function and in the middle the log of 1 minus the distribution function. This gives us more accentuated view of the tail of the distribution. Then in the bottom panel the Monte Carlo results of the VaR approximations are given by means of a box plot using the 5% and 95% percentiles for the box. As before, one million simulations were used to approximate VaR and the VaR calculations were repeated a 1000 times. In Figure 2(b) we assume \n
Illustration of the effects of VaR estimation using the naïve approach. (a) True Burr distribution, T_Burr(1, 0.6, 2), (b) simulated observations from the T_Burr(1, 0.6, 2) distribution with fitted distribution F_Burr(1.07, 0.56, 2.2), (c) augmented simulated observations with fitted distribution F_Burr(1.01, 0.52, 2.26).
In practice, and due to imprecise loss definitions, risk managers may incorrectly group two losses into one extreme loss that has a profound boosting effect on VaR estimates. In the light of this, it is important that the manager is aware of the process generating the data and the importance of clear definitions of loss events.
\nThis modelling approach is based on the mixed model formulation (3). As before, we have available \n
Next, \n
that can be solved to obtain estimates \n
With more than three scenario assessments, fitting techniques can be based on (5) which links the quantiles of the GPD to the scenario assessments. An example would be to minimise \n
Returning now to practical use of Eq. (6), the algorithm below summarises the integration of the historical data with the 1-in-\n
\n
Generate \n
Generate \n
Repeat i and ii \n
When using the GPD 1-in-\n
Illustration of VaR estimates obtained from a GPD fit on the oracle quantiles. (a) True Burr distribution, T_Burr(1, 0.6, 2), (b) fitted distribution F_Burr(1.07, 0.56, 2.2) on simulated data, (c) fitted distribution F_Burr(1.01, 0.52, 2.26) on augmented simulated data.
The GPD fit to the oracle quantiles produce similar box plots, which in turn is very similar to the box plot of the VaR approximations. Clearly the fitted Burr has little effect on the VaR estimates. The VaR estimates obtained through the GPD approach is clearly dominated by the oracle quantiles. Of course, if the assessments are supplied by experts and not oracles the results would differ significantly. This is illustrated when we compare the GPD with Venter’s approach.
\nThe challenge is therefore to find a way of integrating the historical data and scenario assessments such that both sets of information are adequately utilised in the process. In particular, it would be beneficial to have measures indicating whether the experts’ scenario assessments are in line with the observed historical data, and if not, to require them to produce reasons why their assessments are so different. Below we describe Venter’s estimation method that will meet these aims.
\nA colleague, Hennie Venter suggested that, given the quantiles \n
Again\n
Notice that if our estimates were actually exactly equal to what they are estimating, these ratios would all be equal to 1. For example, we would then have \n
Notice again that this estimate is consistent in the sense that it actually reduces to \n
Also note that \n
\n
Generate\n
Generate \n
Repeat i and ii \n
The SLA again sheds some light on this method. As noted above the SLA implies that we need to estimate \n
In order to illustrate the properties of this approach we assume that the true underlying severity distribution is the Burr(1.0, 0.6, 2) as before. We then construct a ‘false’ severity distribution as the fitted distribution to the distorted sample depicted in Figure 2(c), i.e. the Burr(1.00,0.52,2.26). We refer to the true severity distribution as Burr_1 and the false one Burr_2. In Figure 4(a) the box plots of the VaR approximations of the two distributions are given (using the same input for the MC simulations). We then illustrate the performance of the GPD and Venter approach in two cases. The first case assumes that the correct (oracle) quantiles of Burr_1 are supplied, but that the loss data are distributed according to the false distribution Burr_2. In the second case, the quantiles of the false severity distribution are supplied, but the loss data follows the true severity distribution. The box plots of the VaR estimates are given in Figure 4(b) for case 1 and Figure 4(c) for case 2.
\nComparison of VaR results for the GPD and Venter approaches. (a) Naïve approach with correct (T_Burr(1, 0.6, 2)), and false data (F_Burr(1.01, 0.52, 2.26)), (b) Case 1 with correct quantiles and false data, (c) Case 2 with false quantiles and correct data.
The behaviour of the GPD approach is as expected and the box plots corresponds to the quantiles supplied. Clearly the quantiles and not the loss data dictates the results. On the other hand, the Venter approach is affected by both the loss data and quantiles supplied. In the example studied here it seems as if the method is more affected by the quantiles than by the data. This role of the data relative to the quantiles changes positively the more loss data are supplied.
\nIn this section we conduct a simulation study to investigate the effect on the two approaches by perturbing the quantiles of the true underlying severity distributions. We assume the six parameters sets of Table 1 as the true underlying severity distributions and then perturb the quantiles in the following way. For each simulation run, choose three perturbation factors \n
For each combination of parameters of the assumed true underlying Poisson frequency and Burr severity distributions and for each choice of the perturbation size parameter \n
Use the VaR approximation algorithm in the second section to determine the 99.9% VaR for the Burr Type XII with the current choice of parameters. Note that the value obtained here approximately equals the true 99.9% VaR. We refer to this value as the approximately true (AT) VaR.
Generate a data set of historical losses, i.e. generate \n
Add to the historical losses three scenarios \n
Using the historical losses and the three scenarios of item iii), calculate the severity distribution estimate \n
Repeat items i–iv \n
Because we are generally dealing with positively skewed data here, we shall use the median as the principal summary measure. Denote the median of the 1000 AT values by MedAT. Then we construct 90% VaR bands as before for the 1000 repeated GPD and Venter VaR estimates, i.e. \n
VaR bands for different Burr parameter sets and frequency combinations.
From Figure 5, we make the following observations:
\nFor small frequencies (\n
The above information suggest that provided enough loss data is available the Venter approach is the best choice to work.
\nAs stated in the introduction to this chapter, Venter’s method has been implemented by major international banks and approved by the local regulator. Based on this experience, we can share the following implementation guidelines:
Study the loss data carefully with respect to the procedures used to collect the data. Focus should be on the largest losses and one has to establish whether these losses were recorded and classified correctly according to the definitions used.
Experts should be presented with an estimate of \n
The assessments by the expert should be checked with the condition \n
The loss data may be fitted by a wide class of severity distributions. We used SAS PROC SEVERITY in order to identify the five best fitting distributions.
Calculate the ratios \n
For the best fitting distribution, present the ratios that deviate significantly from one to the experts for possible re-assessment. If new assessments are provided, repeat guidelines iii to v once or twice.
Different data sources should be considered. The approaches discussed above assumes one unified dataset for the historical data source. In practice different datasets are included for example internal, external and mixed where the latter is scaled. Estimates of \n
Guideline vi may also be repeated on appropriate mixed (scaled) data sets to select the best distribution type.
\nData Scaling. It is practice in operational risk management to use different data sources for modelling future losses. Banks have been collecting their own data, but realistically, most banks only have between five and ten years of reliable loss data. To address this shortcoming, loss data from external sources can be used by banks in addition to their own internal loss data and controls. External loss data comprises operational risk losses experienced by third parties, including publicly available data, insurance data and consortium data. [16] investigate whether the size of operational risk losses is correlated with geographical region and firm size. They use a quantile matching algorithm to address statistical issues that arise when estimating loss scaling models when subjecting the data to a loss reporting threshold. [13] uses regression analysis based on the GAMLSS (generalised additive models for location scale and shape) framework to model the scaling properties. The severity of operational losses using the extreme value theory is used to account for the reporting bias of the external data losses.
\n\nNo historical data available. In the event of having insufficient historical data available, the GPD approach as discussed above may be used. \n
\nAggregation. To capture dependencies of potential operational risk losses across business lines or event types, the notion of copulas may be used (see [15]). Such dependencies may result from business cycles, bank-specific factors, or cross-dependence of large events. Banks employing more granular modelling approaches may incorporate a dependence structure, using copulas to aggregate operational risk losses across business lines and/or event types for which separate operational risk models are used.
\nIn this chapter, we motivated the use of Venter’s approach whereby the severity distribution may be estimated using historical data and experts’ scenario assessments jointly. The way in which historical data and scenario assessments are integrated incorporates measures of agreement between these data sources, which can be used to evaluate the quality of both. This method has been implemented by major international banks and we included guidelines for its practical implementation. As far as future research is concerned, we are investigating the effectiveness of using the ratios in assisting the experts with their assessments. Also, we are testing the effect of replacing \n
The GPD given by
\nwith \n
The three parameter Burr type XII distribution function
\nwith parameters \n
The authors acknowledge grants received from the National Research Foundation, the Department of Science and Technology and the Department of Trade and Industry. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors, and therefore the National Research Foundation does not accept any liability in regard to them.
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