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Mitotic cells detection for HEp-2 specimen images using threshold-based evaluation scheme
Author(s): Krati Gupta; Arnav Bhavsar; Anil K. Sao
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Paper Abstract

We propose a novel automated strategy for classification of HEp-2 specimens as Mitotic Spindle (MS) or non-Mitotic Spindle (non-MS), which is important for CAD-based Anti-Nuclear Antibody (ANA) detection, in diagnosis of autoimmune disorders. Our strategy is based on the observation that few MS type cells are present in the image along with some other pattern cells in a MS labeled HEp-2 specimen. Hence, the commonly followed majority rule in classification of non-MS cells cannot be applied in this case. We propose that the decision for classifying a specimen as MS or non-MS is based on a pre-defined threshold value on the number of detected MS cells in a specimen. In literature, such evaluation criteria is not clearly analyzed. We note that the MS cells have a distinct visual characteristic, which enables us to use simplistic features representation using the fusion of Gabor and LM filter banks, followed by the Bag-of-words framework and Support Vector Machine (SVM) classification. The experimental results are shown using I3A contest HEp-2 specimen dataset. We achieve 100% True-positive, 5.55% False-positive and 0.97 F-score at the best threshold value of MS. The novel and clearly defined decision strategy makes our approach a good alternative for detection of MS specimen.

Paper Details

Date Published: 6 March 2018
PDF: 9 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810F (6 March 2018); doi: 10.1117/12.2293524
Show Author Affiliations
Krati Gupta, Indian Institute of Technology Mandi (India)
Arnav Bhavsar, Indian Institute of Technology Mandi (India)
Anil K. Sao, Indian Institute of Technology Mandi (India)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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