Extraction of semantic features of histological images for content-based retrieval of images

摘要

Abstract: This paper presents an approach for automatically assign histologically meaningful labels to tissue slide images. This approach is implemented as part of a larger system, I- Browse, which combines iconic and semantic content for intelligent image browsing. Our approach partitioned an input image into a number of subimages. A set of texture features based on Gabor filterings and color histogram which capture the visual characteristics of each of the subimages were computed. These image feature measurements then form the input to a pattern classifier which gives an initial coarse label assignment to subimages based on a hierarchical clustering of these image features. To facilitate supervised training of the classifier, a knowledge elicitation tool was developed which allows a histopathologist to assign histological terms to a sample of sub-images obtained from digitized tissue imags. The initial labels and their spatial distribution were then analyzed by a semantic analyzer with the help of a knowledge base which contains prior knowledge of the expected visual appearance of histological images of an organ. The label assigned to the subimages were successive refined through a process of relevant feedback. !4