Breast mass detection and diagnosis using fused features with density

Article type: Research Article

Authors: Wang, Zhiqiong^{a; f; g} | Huang, Yukun^c | Li, Mo^b | Zhang, Hao^d | Li, Chen^a | Xin, Junchang^{b; *} | Qian, Wei^e

Affiliations: [a] Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China | [b] School of Computer Science and Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning), Northeastern University, China | [c] College of Information Science and Engineering, Northeastern University, China | [d] Department of Breast Surgery, Shengjing Hospital of China Medical University, China | [e] College of Engineering, University of Texas at El Paso, USA | [f] Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., China | [g] Acoustics Science and Technology Laboratory, Harbin Engineering University, China

Correspondence: [*] Corresponding author: Junchang Xin, School of Computer Science and Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning), Northeastern University, China. E-mail: [email protected].

Abstract: BACKGROUND:The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to its morphology and texture features, but also closely related to the density of the tumor. However, in the current research on breast masses detection and diagnosis, people usually use the fusion feature of morphology and texture but neglect density, or only the density feature is considered. Therefore, this paper proposes a method to detect and diagnose the breast mass using fused features with density. METHODS:In this paper, we first propose a method based on sub-region clustering to detect the breast mass. The breast region is divided into sub-regions of equal size, and each sub-region is extracted based on local density feature, after that, an Unsupervised ELM (US-ELM) is used for clustering to complete the mass detection. Second, the feature model is constructed based on the mass. This model is composed of the mass region density feature, morphology feature and texture feature. And Genetic Algorithm is used for feature selection, and the optimized feature model is formed. Finally, ELM is used to diagnose benign or malignant mass. RESULTS:An experiment on the real dataset of 480 mammograms in Northeast China shows that our proposed method can effectively improve the detection and diagnosis accuracy of breast masses, where we obtained 0.9184 precision in detection of breast masses and 0.911 accuracy in diagnosis of breast masses. CONCLUSIONS:We have proposed a mass detection system, which achieves better detection accuracy performance than the existing state-of-art algorithm. We also propose a mass diagnosis system based on the fused features with density, which is more efficient than other feature model and classifier on the same dataset.

Keywords: Density Feature, Extreme Learning Machine, Mammograms, Computer-aided Detection, Computer-aided Diagnosis

DOI: 10.3233/XST-180461

Journal: Journal of X-Ray Science and Technology, vol. 27, no. 2, pp. 321-342, 2019