Automatic Diagnosing of Suspicious Lesions in Digital Mammograms

Abdelali Elmoufidi,Mathieu Lamard,Said Jai-Andaloussi,Gwenole Quellec,Guy Cazuguel,Khalid El,Abderrahim Sekkaki

doi:10.14569/ijacsa.2016.070568

Abdelali Elmoufidi, Mathieu Lamard + Show 5 more

Open Access

https://doi.org/10.14569/ijacsa.2016.070568

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Abstract

Breast cancer is the most common cancer and the leading cause of morbidity and mortality among women’s age between 50 and 74 years across the worldwide. In this paper we’ve proposed a method to detect the suspicious lesions in mammograms, extracting their features and classify them as Normal or Abnormal and Benign or Malignant for diagnosing of breast cancer. This method consists of two major parts: The first one is detection of regions of interest (ROIs). The second one is diagnosing of detected ROIs. This method was tested by Mini Mammography Image Analysis Society (Mini-MIAS) database. To check method’s performance, we’ve used FROC (Free-Receiver Operating Characteristics) curve in the detection part and ROC (Receiver Operating Characteristics) curve in the diagnosis part. Obtained results show that the performance of detection part has sensitivity of 94.27% at 0.67 false positive per image. The performance of diagnosis part has 94.29% accuracy, with 94.11% sensitivity, 94.44% specificity in the classification as normal or abnormal mammogram, and has achieved 94.4%accuracy, with 96.15% sensitivity and 94.54% specificity in the classification as Benign or Malignant mammogram.

Highlights

Breast cancer is the most common cancer and the leading cause of morbidity and mortality among women’s age between 50 and 74 years across the worldwide
The procedure to develop a Computer-AidedDiagnosis (CAD) system, for diagnosing of suspicious regions in mammograms takes place in four steps: 1) Preprocessing step: this step is to prepare the mammograms for the steps of operations; 2)Detection of regions of interest :This step is to analyze the mammogram and extract the necessary information, for example, segmentation which divides the mammogram into multiple segments, edge detection which finds the edges of objects and helps us to find regions of interest; 3) Features extraction and selection of Regions of Interest (ROIs) detected: In this step, we can identify specific patterns, shapes, density and texture; 4) Classification of ROIs: The purpose of this step is to classify the mammograms as Normal or Abnormal and malignant or benign [5][6]
The proposed method checked by mini Mammography Image Analysis Society database[24] and implemented using Seed Region Growing (SRG) algorithm, Local Binary Pattern (LBP) algorithm and support vector machine (SVM) classifier

Summary

Introduction

Breast cancer is the most common cancer and the leading cause of morbidity and mortality among women’s age between 50 and 74 years across the worldwide. The mammography remains the best and most accurate tool for early detection of breast cancer [2],[3]. To automatically detect breast lesions through Computer Aided detection systems (commonly referred CADe). To automatically interpret mammograms through Computer Aided Diagnostic Systems (commonly referred CADx). These systems are employed as a supplement to the radiologists’ assessment. We’ve proposed an automatic method to detect and diagnosing of suspicious lesions in mammogram. The proposed method is a very accurate technique for detecting and diagnosing breast cancer by using mammogram. Obtained results show the efficiency of projected method and make sure chance of its use in rising breast cancer detection and the diagnosing. Paper organization : The rest of paper organized as follows: Section I: An introduction ; Section II: Related work; Section III: Materials and method ; Section IV : Features generation and extraction; Section V : Our proposed research; Section VI : Results and performance of proposed method ; Section VII : Conclusion; and references are given at the end

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