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Proceedings Paper

Automated detection of microcalcification clusters in mammograms
Author(s): Vikrant A. Karale; Sudipta Mukhopadhyay; Tulika Singh; Niranjan Khandelwal; Anup Sadhu
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Paper Abstract

Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342R (3 March 2017); doi: 10.1117/12.2254330
Show Author Affiliations
Vikrant A. Karale, Indian Institute of Technology Kharagpur (India)
Sudipta Mukhopadhyay, Indian Institute of Technology Kharagpur (India)
Tulika Singh, Postgraduate Institute of Medical Education & Research (India)
Niranjan Khandelwal, Postgraduate Institute of Medical Education & Research (India)
Anup Sadhu, Medical College and Hospital Kolkata (India)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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