Histological images are the key ingredients in medical diagnosis and prognosis in today's medical field. They are imagery acquired by analysts from microscopy to determine the cellular structure and composition of a patient's biopsy. This thesis provides an approach to analyze the histological segmentation obtained from histological images using shape distributions and provides a computationally feasible method to predict their histological grade.
This process provides a way of generating suggestions using segmented images in a way that is independent of the segmentation process. The process generates histograms for each image that describes a set of shape distributions generated from eight metrics that we have devised. The shape distributions are extracted from a learning set that the user provides. The shape distributions are then analyzed by querying a classification for each case using K-nearest-neighbor. The quality of the classifications is measured by a composite measure composed of precision and recall based on the query.