Semantic content analysis and annotation of histological images

This paper presents a novel two-dimensional (2-D) stochastic method for semantic analysis of the content of histological images Specifically, we propose a 2-D generalization of the traditional hidden Markov model (HMM). The generalization is called spatial-hidden Markov model (SHMM) that captures the contextual characteristics of complex biological features in histological images The model employs a second-order neighborhood system and assumes the conditional independence of vertical and horizontal transitions between hidden states. The notion of ‘past’ in SHMM is defined as what have been observed in a row-wise raster scan. This paper focuses on two fundamental problems: the best states decoding problem and the estimation of generation probability of an image by a SHMM. Based on our independence assumption of horizontal and vertical transitions, we derive computational tractable solutions to those problems. These solutions are direct extensions of their counterparts, i.e., the Viterbi algorithm and Forward–Backward algorithm, for 1-D HMM. Our experiments were carried on a medical image database with 200 images and compared with a state-of-the-art approach that was run on the same database. The annotation results demonstrated that SHMM consistently outperforms the previous approach and ameliorates many of its drawbacks. In addition, performance comparison with HMM has also validated the superiority of SHMM.