Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis

Mohamed W Abo El-Soud,Imad Zyout,Khalid M Hosny,Mohamed Meselhy Eltoukhy

doi:10.1109/access.2020.3008038

Abstract

Masses are mammographic nonpalpable signs of breast cancer. These masses could be detected using screening mammography. This paper proposed a system utilizing orthogonal moment invariants (OMIs) features for mammographic masses detection and diagnosis. In this work, three sets of OMIs features were extracted. These OMIs features are Gaussian-Hermite moments (GHMs), Gegenbauer moments (GeMs), and Legendre moments (LMs). The extracted features are fused and presented to the particle swarm optimization (PSO) algorithm for feature selection. The classification step is achieved using the support vector machine (SVM). The proposed system is evaluated using 400 regions, extracted from the DDSM dataset. The obtained results reveal the promising application of OMIs features for masses detection and identification. It shows that fusing the OMIs features produces an acceptable detection performance where the area under the receiver operating characteristics (ROC) curve is $Az=0.969\pm 0.01$ and the best performance of OMIs features is $Az = 0.856\pm 0.053$ for characterizing the malignancy of masses.

Highlights

Breast cancer is the most frequent cancer among women worldwide
particle swarm optimization (PSO)-support vector machine (SVM) SETUP In this work, the PSO-SVM settings include the fitness criterion based on the Az-value of the receiver operating characteristics (ROC) curve, the swarm size is set to 100 particles structured as described in Section III, and the maximum number of iteration of 50
EXPERIMENTAL RESULTS AND DISCUSSION For evaluating and obtaing the results of the applying the proposed orthogonal moment invariants (OMIs) features to distinguish between abnormal and normal classes (CADe or detection) and to differentiate malignant from benign classes (CADx or diagnosis), we used a set of mammographic regions of interest (ROIs) extracted from the digital dataset for screening mammography (DDSM) [28]

Summary

INTRODUCTION

Breast cancer is the most frequent cancer among women worldwide. It is impacting 2.1 million women each year. Khehra and Pharwaha [17] developed a comparison study between three different metaheuristic methods, namely GA, PSO, and biogeography based optimization (BBO), to select an optimal set of features from 50 features In their work, they reported a 91% classification rate is using PSO-SVM. In this study, we hypothesized that it is possible to identify and fuse the global and local features computed from the ROIs of mammographic masses These moment-based features could produce a high-performance CADe/CADx. Besides, applying the PSO could reduce the requirement of an extensive training dataset as the conventional deep learning approach. The objective of this study is to analyze the combination of three moment-based features to find out the best set of features that have the capability to distinguish between different mammographic masses, Either normal or abnormal, and the abnormal class is distinguished into benign or malignant.

ORTHOGONAL MOMENT INVARIANTS

THE PROPOSED CAD SYSTEM

EXPERIMENTAL RESULTS AND DISCUSSION

CONCLUSION