@article{Milosevic2014701,
title = {Segmentation for the enhancement of microcalcifications in digital mammograms},
author = {Marina Milosevic and Dragan Jankovic and Aleksandar Peulic},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911095838\&doi=10.3233%2fTHC-140841\&partnerID=40\&md5=c3c8c3b5f3d807110fc8c55b2fdf25f2},
doi = {10.3233/THC-140841},
year = {2014},
date = {2014-01-01},
journal = {Technology and Health Care},
volume = {22},
number = {5},
pages = {701 \textendash 715},
publisher = {IOS Press},
abstract = {Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the contrast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique. © 2014 - IOS Press and the authors. All rights reserved.},
keywords = {Algorithms; Breast Diseases; Breast Neoplasms; Calcinosis; Female; Humans; Mammography; Radiographic Image Interpretation, Computer-Assisted; Wavelet Analysis; algorithm; Breast Diseases; Breast Neoplasms; calcinosis; computer assisted diagnosis; female; human; mammography; procedures; wavelet analysis},
pubstate = {published},
tppubtype = {article}
}
