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Proceedings Paper

A human visual based binarization technique for histological images
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Paper Abstract

In the field of vision-based systems for object detection and classification, thresholding is a key pre-processing step. Thresholding is a well-known technique for image segmentation. Segmentation of medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), X-Ray, Phase Contrast Microscopy, and Histological images, present problems like high variability in terms of the human anatomy and variation in modalities. Recent advances made in computer-aided diagnosis of histological images help facilitate detection and classification of diseases. Since most pathology diagnosis depends on the expertise and ability of the pathologist, there is clearly a need for an automated assessment system. Histological images are stained to a specific color to differentiate each component in the tissue. Segmentation and analysis of such images is problematic, as they present high variability in terms of color and cell clusters. This paper presents an adaptive thresholding technique that aims at segmenting cell structures from Haematoxylin and Eosin stained images. The thresholded result can further be used by pathologists to perform effective diagnosis. The effectiveness of the proposed method is analyzed by visually comparing the results to the state of art thresholding methods such as Otsu, Niblack, Sauvola, Bernsen, and Wolf. Computer simulations demonstrate the efficiency of the proposed method in segmenting critical information.

Paper Details

Date Published: 10 May 2017
PDF: 10 pages
Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 102210Q (10 May 2017); doi: 10.1117/12.2262815
Show Author Affiliations
Shreyas Kamath K. M., Tufts Univ. (United States)
Rahul Rajendran, Tufts Univ. (United States)
Karen Panetta, Tufts Univ. (United States)
Sos Agaian, The Univ. of Texas at San Antonio (United States)
City Univ. of New York (United States)

Published in SPIE Proceedings Vol. 10221:
Mobile Multimedia/Image Processing, Security, and Applications 2017
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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