Open AccessMultiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm
Author(s) -
Aminah Abdul Malek,
Nurhanani Abdul Rahim,
Nor Farah Nabilah Mushtafa,
Nadhirah Afiqah Zailan,
N. S. Mohamed
Publication year - 2021
Publication title -
international journal of software engineering and computer systems
Language(s) - English
Resource type - Journals
eISSN - 2289-8522
pISSN - 2180-0650
DOI - 10.15282/ijsecs.7.1.2021.1.0077
Subject(s) - jaccard index , sørensen–dice coefficient , microcalcification , segmentation , artificial intelligence , parametric statistics , cut , pattern recognition (psychology) , kernel (algebra) , mammography , graph , computer science , image segmentation , dice , mathematics , algorithm , computer vision , breast cancer , statistics , combinatorics , medicine , theoretical computer science , cancer
Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.