Modern advances in imaging technology have enabled the collection of huge amounts of multimodal imagery of complex biological systems. The extraction of information from this data and subsequent analysis are essential in understanding the architecture and dynamics of these systems. Due to the sheer volume of the data, manual annotation and analysis is usually infeasible, and robust automated techniques are the need of the hour. In this dissertation, we present three hidden Markov model (HMM)-based methods for automated analysis of multimodal biomedical images. First, we outline a novel approach to simultaneously classify and segment multiple cells of different classes in multi-biomarker images. A 2D HMM is set up on the superpixel lattice obtained from the input image. Parameters ensuring spatial consistency of labels and high confidence in local class selection are embedded in the HMM framework, and learnt with the objective of maximizing discrimination between classes.

Optimal labels are inferred using the HMM, and are aggregated to obtain global multiple object segmentation. We then address the problem of automated spatial alignment of images from different modalities. We propose a probabilistic framework, constructed using a 2D HMM, for deformable registration of multimodal images. The HMM is tailored to capture deformation via state transitions, and modality-specific representation via class-conditional emission probabilities. The latter aspect is premised on the realization that different modalities may provide very different representation for a given class of objects. Parameters of the HMM are learned from data, and hence the method is applicable to a wide array of datasets. In the final part of the dissertation, we describe a method for automated segmentation and subsequent tracking of cells in a challenging target image modality, wherein useful information from a complementary (source) modality is effectively utilized to assist segmentation.

Labels are estimated in the source domain, and then transferred to generate preliminary segmentations in the target domain. A 1D HMM-based algorithm is used to refine segmentation boundaries in the target image, and subsequently track cells through a 3D image stack. This dissertation details techniques for classification, segmentation and registration, that together form a comprehensive system for automated analysis of multimodal biomedical datasets.