Dimiter Prodanov

By Dimiter Prodanov and Kris Verstreken

Part of the book: Molecular Imaging

By Ponnapat Watjanatepin and Dimiter Prodanov

Engineered nanomaterials (ENM) are used in a wide variety of applications: from cosmetics and paints to sportswear and semiconductor chips. While for chemicals there are established regulatory frameworks dealing with the risk for the consumers, workers, and the environment, this is not the case for nanomaterials. This is precisely why ENMs are used—the properties of matter change at the nanoscale and become dependent on the particle morphology and size. Our understanding on how such nano-systems react with biological matter, such as cells and tissues, is far from complete, and this brings about an increasing level of uncertainty in the research and development process. This chapter will give an overview of several materials, which are either used or have potential applications in nanoelectronics. While silicon dioxide and metal oxide nanoparticles are used in semiconductor processing in standard polishing steps, applications of carbon materials may be more disruptive. As promising materials with broad applications, we focus on carbon nanotubes and graphene. So-identified materials are used to illustrate the use of different risk assessment tools in the occupational setting of nanoelectronics. The application of such tools in itself is also a growing area of research efforts supported by international stakeholders, such as the European Commission.

Part of the book: Occupational Health and Safety

By Dimiter Prodanov

The chapter introduces multiscale methods for image analysis and their applications to segmentation of microscopic images. Specifically, it presents mathematical morphology and linear scale-space theories as overarching signal processing frameworks without excessive mathematical formalization. The chapter introduces several differential invariants, which are computed from parametrized Gaussian kernels and their derivatives. The main application of this approach is to build a multidimensional multiscale feature space, which can be subsequently used to learn characteristic fingerprints of the objects of interests. More specialized applications, such as anisotropic diffusion and detection of blob-like and fiber-like structures, are introduced for two-dimensional images, and extensions to three-dimensional images are discussed. Presented approaches are generic and thus have broad applicability to time-varying signals and to two- and three-dimensional signals, such as microscopic images. The chapter is intended for biologists and computer scientists with a keen interest in the theoretical background of the employed techniques and is in part conceived as a tutorial.

Part of the book: Advances in Neural Signal Processing