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Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners., Competing Interests: The authors have declared that no competing interests exist.

Histology is the study of the structure of biological tissue using microscopy techniques. As digital imaging technology advances, high resolution microscopy of large tissue volumes is becoming feasible; however, new interactive tools are needed to explore and analyze the enormous datasets. In this paper we present a visualization framework that specifically targets interactive examination of arbitrarily large image stacks. Our framework is built upon two core techniques: display-aware processing and GPU-accelerated texture compression. With display-aware processing, only the currently visible image tiles are fetched and aligned on-the-fly, reducing memory bandwidth and minimizing the need for time-consuming global pre-processing. Our novel texture compression scheme for GPUs is tailored for quick browsing of image stacks. We evaluate the usability of our viewer for two histology applications: digital pathology and visualization of neural structure at nanoscale-resolution in serial electron micrographs.

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.

In this article we have presented the application of three region based segmentation techniques namely, seeded volume growing, constrained erosion-dilation techniques and 3-D watershed algorithm. The algorithms are suitably extended to apply on 3-D histo-pathological images. Suitable modifications and extension for each algorithm is done to obtain better segmentation. A quantitative as well as qualitative comparison of the three methods is presented. Modifications to these algorithms for obtaining better results are discussed. The modifications include, (1) design of adaptive similarity measures to control the seeded volume growing and (2) rule-based merging of the over-segmented cells in the case of the 3-D watershed algorithm. Some results and quantitative study is also presented.