Texture based classification of the severity of mitral regurgitation. (1st June 2016)

• Record Type: Journal Article
• Authors: Balodi, Arun; Dewal, M.L.; Anand, R.S.; Rawat, Anurag
• Is Part Of: Computers in biology and medicine. Volume 73(2016)
• Page Start: 157
• Page End: 164
• Publication Date: 2016-06-01
• Subjects: Echocardiography -- Valvular regurgitation -- Texture feature -- Multiclass support vector machine (SVM) -- Computer aided diagnostic (CAD) system; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.04.013
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Clinically, the severity of valvular regurgitation is assessed by manual tracing of the regurgitant jet in the respective chambers. This work presents a computer-aided diagnostic (CAD) system for the assessment of the severity of mitral regurgitation (MR) based on image processing that does not require the intervention of the radiologist or clinician. Eight different texture feature sets from the regurgitant area (selected through an arbitrary criterion) have been used in the present approach. First order statistics have been used initially, however, observing their limitations, the other texture features such as spatial gray level difference matrix, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws' textures energy measure, fractal dimension texture analysis and Fourier power spectrum have additionally been used. For the classification task a supervised classifier i.e., support vector machine has been used in the present approach. The classification accuracy has been improved significantly by using these texture features in combination, in comparison to when fed individually as input to the classifier. The classification accuracy of 95.65±1.09, 95.65±1.09 and 95.36±1.13 has been obtained in apical two chamber, apical four chamber and parasternal long axis views, respectively. Therefore, the results of this paper indicate that the proposed CAD system may effectively assist the radiologists in establishing (confirming) the MR stages, namely, mild, moderate and severe. Highlights: A CAD system is proposed for the classification of mitral regurgitation. Experimental results show the effectiveness of the proposed method. Reduces observer based variations leading to either over or under estimation of MR. The approach results in true classification with high specificity and sensitivity. It saves a useful amount of clinician time in the process.