Detection of Microcalcifications in Mammography using Image Processing
DOI:
https://doi.org/10.21439/jme.v8i1.115Keywords:
Detection of microcalcifications. Computer vision. Computer-assisted diagnosis.Abstract
Breast cancer is the most diagnosed type of cancer and the one that causes the most deaths in women in the world. Mammograms allow the detection of microcalcifications at an early stage. The objective of this work is to develop a computer vision system to detect microcalcifications in mammography images, for this purpose images from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) database are used. The detection algorithm is divided into two parts, preprocessing and segmentation. A region of interest was not defined, as it is considered that the entire breast is subject to the appearance of microcalcifications. The programming language used is Python, together with the Numpy and OpenCV libraries. For validation, accuracy, sensitivity, specificity, positive predictive value and Dice Similarity were calculated. The experiments show that the accuracy of the method is 98.2\%, the sensitivity is 49.6\%, the specificity is 89.3\%, the positive predictive value is 78.7\% and the Similarity Dice is 52.7\%. The developed system achieved the desired objective, with good performance.
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