. . . . . "The analysis of protein-level multigene expression signature maps computed from the fusion of differently stained immunohistochemistry images is an emerging tool in cancer investigation. Creating these maps requires registering sets of histological images, a challenging task due to their large size, the non-linear distortions existing between consecutive sections and to the fact that the images correspond to different histological stains and thus, may have very different appearance. This thesis proposal discusses the registration of differently stained consecutive histological sections together with typical image size of several thousands times several thousands pixels. The main idea is to do the segmentation farst and then run the registration on the segmented images which should be faster (simpler similarity metric to evaluate or e.g. kind of contour registration) and more robust. So far, we have preferred the segmentation and registration of stained histological sections independently. Later on we would like to do both processes simultaneously. The thesis proposal briefly summarises the state-of-the-art methods, mainly focusing on registration. We present our histological images and also synthetic datasets we have designed to simulate the real images. We discuss our existing unsupervised multi-class segmentation method and new similarity metric measure for registration of segmented images. In the end, we discuss future work and propose the future research directions." . . . "Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal"@en . . "RIV/68407700:21230/13:00211382" . . . "P(GAP202/11/0111), S" . . "The analysis of protein-level multigene expression signature maps computed from the fusion of differently stained immunohistochemistry images is an emerging tool in cancer investigation. Creating these maps requires registering sets of histological images, a challenging task due to their large size, the non-linear distortions existing between consecutive sections and to the fact that the images correspond to different histological stains and thus, may have very different appearance. This thesis proposal discusses the registration of differently stained consecutive histological sections together with typical image size of several thousands times several thousands pixels. The main idea is to do the segmentation farst and then run the registration on the segmented images which should be faster (simpler similarity metric to evaluate or e.g. kind of contour registration) and more robust. So far, we have preferred the segmentation and registration of stained histological sections independently. Later on we would like to do both processes simultaneously. The thesis proposal briefly summarises the state-of-the-art methods, mainly focusing on registration. We present our histological images and also synthetic datasets we have designed to simulate the real images. We discuss our existing unsupervised multi-class segmentation method and new similarity metric measure for registration of segmented images. In the end, we discuss future work and propose the future research directions."@en . . "21230" . . "104418" . . "1"^^ . "Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal" . . . "RIV/68407700:21230/13:00211382!RIV14-MSM-21230___" . "segmentation; registration; stained; histology"@en . "Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal"@en . "Borovec, Ji\u0159\u00ED" . "1"^^ . . "[E4DB063C613D]" . "Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal" . .