Multiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm

Aminah Abdul Malek,Norlyda Mohamed,Nurhanani Abdul Rahim,Nor Farah Nabilah Mushtafa,Nadhirah Afiqah Zailan

doi:10.15282/ijsecs.7.1.2021.1.0077

Abstract

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.

Highlights

Breast cancer is the most prevalent cancer, ranking second worldwide and becoming the leading mortality cause among women [1]
Since there were two clusters that have been set up to, the image is segmented according to these regions at the phase where this paper focused on multiregional segmentation of mammograms image by using Parametric Kernel Graph Cut Algorithm
The main purpose of this paper was to segment the microcalcification on mammogram images by using Parametric Kernel Graph Cut Algorithm

Summary

Introduction

Breast cancer is the most prevalent cancer, ranking second worldwide and becoming the leading mortality cause among women [1]. The survival rate is enormously enhanced if breast abnormalities are detected at an early stage. Mammography is widely used as a gold standard in early detection of breast cancer [2]. The mammography images make it probable to abnormalities in the breast such as microcalcification. Microcalcification is a tiny deposit of calcium that has hoarded in the breast tissue tends to make the suspected region not seen through complementary views on the mammograms. Radiologists only diagnose them visually which may lead to human errors, detection errors, and this can lead to late detection. The mammogram image containing microcalcification must undergo segmentation process where the image can be transformed into more significant for evaluation purposes

Objectives

Results

Conclusion