Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer. (October 2019)

• Record Type: Journal Article
• Authors: Chen, Xuxin; Zargari, Abolfazl; Hollingsworth, Alan B; Liu, Hong; Zheng, Bin; Qiu, Yuchen
• Is Part Of: Computer methods and programs in biomedicine. Volume 179(2019)
• Publication Date: 2019-10
• Subjects: Computer-aided diagnosis (CAD) -- Classification of mammograms -- Quantitative image feature analysis -- Support vector machine (SVM) -- Particle swarm optimization (PSO) algorithm; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.104995
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel image marker is developed for predicting the malignant lesion depicted on digital mammograms. 59 features are extracted from the whole breast area to generate the marker. We initially demonstrate that the new marker enables to effectively distinguish the benign and malignant lesions. Abstract: Background and Objective: This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer. Methods: From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. Next, for each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training a support vector machine (SVM) classifier to generate a final score for predicting likelihood of the case being malignant. To test the scheme, we assembled a dataset involving 275 patients who had biopsy due to the suspicious findings on mammograms. Among them, 134 are malignant and 141 are benign. A ten-fold cross validation method was used to train and test the scheme. Results: The classification performance levels measured by the areas under ROC curves are 0.79 ± 0.07 and 0.75 ± 0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. Conclusions: This study demonstrates feasibility of developing a new global mammographic image feature analysis-based scheme to predict the likelihood of case being malignant without lesion segmentation.