A novel approach for cirrhosis recognition via improved LBP algorithm and dictionary learning. (September 2017)

• Record Type: Journal Article
• Authors: Lei, Yiming; Zhao, Ximei; Wang, Guodong; Yu, Kexin; Guo, Weidong
• Is Part Of: Biomedical signal processing and control. Volume 38(2017)
• Page Start: 281
• Page End: 292
• Publication Date: 2017-09
• Subjects: Cirrhosis -- T-LBP -- T-LBPs -- ELM -- K-SVD -- Dictionary learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.06.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We proposed an improved LBP algorithm and its T-LBPs feature which describes the texture of cirrhosis better. Experiments using T-LBP transform performs better than those without it. We have proved that the consuming time of SVM and kernel-ELM is less than that of Basic ELM in cirrhosis recognition issue. We have applied K-SVD dictionary learning method in cirrhosis recognition, we use the sparse coefficients as the input of classifiers such as SVM and ELM. The accuracy is the highest compared with traditional methods. Moreover, the error of updating dictionary decreases sharply through T-LBP transform, so the obtained dictionary has better ability of reconstructing the original samples. Abstract: Early diagnosis of cirrhosis has been increasing the interest of medical specialists and engineers. Cirrhosis diagnosis is difficult to distinguish with naked eyes, and it depends on subjectivity of physicians largely. In this paper, the improved Local Binary Pattern(LBP) algorithm called T-LBP(total LBP) and its corresponding T-LBPs(T-LBP spectrum) feature were proposed to describe cirrhosis texture and to solve the edge blurring problem caused by cirrhosis effectively. We applied fusion of T-LBPs, two-dimensional Gabor transform and K-SVD(single value decomposition which generalizes K-means clustering process) based dictionary learning methods in cirrhosis recognition of ultrasound(US) images for the first time. Advantages of proposed algorithms include, firstly, to our best knowledge, proposed T-LBPs feature outperforms the traditional features using support vector machine(SVM), and it has also been proved that the consuming time of kernel extreme learning machine(kernel-ELM) is less than that of basic ELM in this issue; secondly, dictionary learning based recognition method through T-LBP has obtained the highest recognition rate of 99.69% compared with state-of-the-art methods, and dictionary updating error decreased sharply via T-LBP. Therefore, the proposed algorithm will contribute to the clinical cirrhosis diagnosis.