Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation. (July 2015)

• Record Type: Journal Article
• Authors: Cohen, Assaf; Rivlin, Ehud; Shimshoni, Ilan; Sabo, Edmond
• Is Part Of: Computerized medical imaging and graphics. Volume 43(2015)
• Page Start: 150
• Page End: 164
• Publication Date: 2015-07
• Subjects: Microscopy -- Histology -- Colon crypts -- Segmentation -- Active contour; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.12.006
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: Improve classification accuracy using two-step pixel-level classification. Memory based active contour that is robust to pixel-level classification errors. Test 7180 crypts: 87% and 9% true and false positives, 96% segmentation accuracy. We believe this method is general and can be applied on glandular biopsies. Abstract: In this paper, we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%.