{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T01:27:16Z","timestamp":1699838836796},"reference-count":20,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,7,1]]},"abstract":"<p>The pectoral muscle represents a predominant density region in Medio-Lateral Oblique (MLO) views of mammograms, which appears at approximately the same density as the dense tissues of interest in the image and can affect the results of image analysis methods. Therefore, segmentation of pectoral muscle is important in order to limit the search for the breast abnormalities only to the breast region. In this paper, a simple and effective approach is proposed to exclude the pectoral muscle based on binary operation. The performance is analyzed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on differences between the results received from the radiologists and by the proposed method. The digital mammogram images are taken from MIAS dataset which contains 322 images in total, out of which the proposed algorithm able to detect and remove the pectoral region from 291 images successfully.<\/p>","DOI":"10.4018\/ijcvip.2012070102","type":"journal-article","created":{"date-parts":[[2013,2,6]],"date-time":"2013-02-06T16:19:52Z","timestamp":1360167592000},"page":"21-29","source":"Crossref","is-referenced-by-count":4,"title":["Removal of Pectoral Muscle Region in Digital Mammograms using Binary Thresholding"],"prefix":"10.4018","volume":"2","author":[{"given":"A. Kaja","family":"Mohideen","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics & Computational Sciences, PSG College of Technology, Coimbatore, India"}]},{"given":"K.","family":"Thangavel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Periyar University, Salem, India"}]}],"member":"2432","reference":[{"key":"ijcvip.2012070102-0","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-5318-8_51"},{"key":"ijcvip.2012070102-1","doi-asserted-by":"crossref","unstructured":"Chandrasekhar, R., & Attikiouzel, Y. (2000). New range-based neighborhood operator for extracting edge and texture information from mammograms for subsequent image segmentation and analysis. 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R., & Sadri, S. (2007). Pectoral muscle segmentation on digital mammograms by nonlinear diffusion filtering. In Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (pp. 581-584).","DOI":"10.1109\/PACRIM.2007.4313303"},{"key":"ijcvip.2012070102-14","doi-asserted-by":"publisher","DOI":"10.1109\/42.938247"},{"key":"ijcvip.2012070102-15","unstructured":"Sinha, P. K., Udupa, J. K., Conant, E. F., & Chakraborty, D. P. (1993). Near automatic quantification of breast tissue glandularity via digitized mammograms. 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