In this talk we will consider significant characterizations of pixels in medical images to define the similarity relation between them. These are based on textual information and the shortest paths in the graph representation of the images.
I will present how to distinguish placenta structure and pathological conditions, while the shape of villi cross-section can be complicated and not regular and intensity of colors depends on a lot of factors e.g. biological features, physicochemical parameters of tissues, methods of microscopy samples preparation. Next, we will discuss cluster analysis which is an approach to segmentation problems. The main purpose is grouping or segmenting a collection of objects (pixels) into subsets which are called clusters. Objects of each cluster are more closely related to one another than objects assigned to other clusters. Additionally, the goal is to arrange clusters into a natural hierarchy. This approach is based on repeatedly grouping clusters themselves at each level of the hierarchy.
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