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Updated: Aug 19, 2026
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Published on: April 8, 2015
Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine
Dimitris Glotsos1, Panagiota Spyridonos, Dionisis Cavouras
1Medical Image Processing and Analysis Laboratory, Department of Medical Physics, School of Medicine, University of Patras, Rio, Patras, Greece. dimglo@med.upatras.gr
Objective:
To develop a method for the automated segmentation of images of routinely hematoxylin-eosin (H-E)-stained microscopic sections to guarantee correct results in computer-assisted microscopy.
Study Design:
Clinical material was composed 50 H-E-stained biopsies of astrocytomas and 50 H-E-stained biopsies of urinary bladder cancer. The basic idea was to use a support vector machine clustering (SVMC) algorithm to provide gross segmentation of regions holding nuclei and subsequently to refine nuclear boundary detection with active contours. The initialization coordinates of the active contour model were defined using a SVMC pixel-based classification algorithm that discriminated nuclear regions from the surrounding tissue. Starting from the boundaries of these regions, the snake fired and propagated until converging to nuclear boundaries.
Results:
The method was validated for 2 different types of H-E-stained images. Results were evaluated by 2 histopathologists. On average, 94% of nuclei were correctly delineated.
Conclusion:
The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images.
