Journal
MEDICAL IMAGE ANALYSIS
Volume 17, Issue 8, Pages 1206-1219Publisher
ELSEVIER
DOI: 10.1016/j.media.2013.07.007
Keywords
Histology; Skeletal muscle; Image segmentation; Machine learning; Clump splitting
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Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until now this has not been the case when applied to skeletal muscle histology images. We introduce CHARISMA, a new top-down cell segmentation framework for histology images which combines image processing techniques, a supervised trained classifier and a novel robust clump splitting algorithm. We evaluate our framework on real-world data from intensive care unit patients. Considering both segmentation and cell property distributions, the results obtained by our method correspond well to the ground truth, outperforming other examined methods. (C) 2013 Elsevier B.V. All rights reserved.
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