Biomedical image segmentation for semantic visual feature extraction

Biomedical photographs comprise diverse optically acquired images. Accurate classification into meaningful subclasses is valuable in biomedical image retrieval systems. Conventional visual descriptors are limited in their ability to assign semantic labels to images for meaningful retrieval. In this paper we propose a Markov random field (MRF)-based biomedical image segmentation method to segment images into meaningful regions that can be associated with semantic labels. We focus on several tissue image types and develop two MRF models: (i) for tissue image detection from large photograph collection; and, (ii) for region segmentation and semantic labeling. Experimental results demonstrate that our method can detect tissue images in about 82% precision, and our proposed visual descriptors computed from the segmentation results outperform existing visual descriptors. This latter result can be effectively used in biomedical image retrieval systems for retrieving tissue images.