Medical Image Segmentation with Split-and-Merge Method

The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. One of the most promising methods is region growing but it is quite memory intensive. The size of high-resolution tissue images can easily reach the order of a hundred megabytes therefore the memory requirement for the region growing is more than one gigabyte. To provide the execution in low-end clients we have to split the whole image into smaller tiles and after the processing of each individual tiles we have to merge the results.

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Parameter assisted HE colored tissue image classification

The aim of our work was to design and implement a software solution, which supports quantitative histological analysis of hematoxilin eozin (HE) stained colon tissue samples, identify tissue structures – nuclei, glands and epithelium – using image processing methods. Furthermore, based on the result of the histological segmentation, it gives a suggestion for the negative or malignant status of the samples automatically. In this paper we describe the algorithm which builds up mainly by two software components: MorphCheck -our software framework-, which is capable to make effective, morphometric evaluation of high resolution digital tissue images and a modified WND-CHARM (Weighted Neighbor Distance Using Compound Hierarchy of Algorithms Representing Morphology), which is a multi-purpose image classifier. The image classification was performed mainly based on 75+15 pre-defined colon tissue specific parameters, which were measured by MorphCheck, and other 2873 in-built generic image parameters, which were measured by WND-CHARM. We appended WND-CHARM’s learning and classification capabilities with our colon tissue specific parameters and with this act we have increased its classification accuracy significantly on HE stained colon tissue sample images.

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