Umamaheswari K

By Umamaheswari Kumarasamy, G.V. Shrichandran and A. Vedanth Srivatson

Topical review of recent trends in Modeling and Regularization methods of Diffuse Optical Tomography (DOT) system promotes the optimization of the forward and inverse modeling methods which provides a 3D cauterization at a faster rate of 40frames/second with the help of a laser torch as a hand-held device. Analytical, Numerical and Statistical methods are reviewed for forward and inverse models in an optical imaging modality. The advancement in computational methods is discussed for forward and inverse models along with Optimization techniques using Artificial Neural Networks (ANN), Genetic Algorithm (GA) and Artificial Neuro Fuzzy Inference System (ANFIS). The studies carried on optimization techniques offers better spatial resolution which improves quality and quantity of optical images used for morphological tissues comparable to breast and brain in Near Infrared (NIR) light. Forward problem is based on the location of sources and detectors solved statistically by Monte Carlo simulations. Inverse problem or closeness in optical image reconstruction is moderated by different regularization techniques to improve the spatial and temporal resolution. Compared to conventional methods the ANFIS structure of optimization for forward and inverse modeling provides early detection of Malignant and Benign tumor thus saves the patient from the mortality of the disease. The ANFIS technique integrated with hardware provides the dynamic 3D image acquisition with the help of NIR light at a rapid rate. Thereby the DOT system is used to continuously monitor the Oxy and Deoxyhemoglobin changes on the tissue oncology.

Part of the book: Digital Image Processing Applications

By Umamaheswari K, Shrichandran G.V. and Jeba Derwin D.

Diffuse optical tomography (DOT) is favorable to analyze physical records in organic tissue with a specific purpose by means of a method related to the forward problem and the inverse solution. This study develops morphological soft tissue realization using an image reconstruction algorithm constructed on multifrequency DOT in Near-Infra-Red (NIR) wavelength. Forward problem solves the Diffusion Equation to compute the optical flux distributed in the phantom geometrical model. Inverse solution, the image is reconstructed using the absorption and reduced scattered coefficients under different boundary conditions. The inverse image reconstruction algorithm is tested for several simulation, with variation in background contrast ratios for different frequencies are simulated. The image reconstruction in DOT eliminates spatial resolution by optimizing source-detector separation and modulation intensities of the source.

Part of the book: Biosignal Processing