Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms  

作　　者：Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar 

机构地区：[1]Department of Electrical,Computer and Telecommunications Engineering,Botswana International University of Science and Technology,Palapye,Private Bag 16,Botswana [2]College of Computer and Information Sciences,Prince Sultan University,Riyadh,11586,Saudi Arabia [3]Robotics and Internet of Things Lab,Prince Sultan University,Riyadh,11586,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第9期4749-4765,共17页计算机、材料和连续体（英文）

基　　金：supported by the research grant(SEED-CCIS-2024-166),Prince Sultan University,Saudi Arabia。

摘　　要：Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关 键 词：Breast cancer CAD machine learning ROI segmentation THERMOGRAPHY 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]