Abstract
The grading evaluation of metacarpophalangeal rheumatoid arthritis (RA) ultrasonic images is a diagnostic challenge that heavily relies on the expertise of trained sonographers. This study presents a grading method for detecting and estimating the geometric and texture features of synovium thickening and bone erosion. Unlike previous studies in this area, this work uses the metrics and texture features of region of interest (ROI). The highlighted feature of metacarpophalangeal bone and the dark feature of the synovial thickening are extracted simultaneously by the segmented method based on the Gaussian scale space. The segmented results are analyzed to extract three quantitative geometric parameters, which are combined with gray-level co-occurrence matrix (GLCM) statistic texture features to describe the ultrasonic image of metacarpophalangeal RA. To obtain the preferable ability of classification, we applied a support vector machine (SVM) and various feature descriptors, including GLCM, local binary patterns (LBP), and GLCM + LBP, to grade the ultrasonic image of metacarpophalangeal RA. Results show that the SVM, based on our feature descriptor, provides the highest accuracy of up to 92.50%, of the four descriptors. The SVM based on GLCM+LBP descriptor shows better accuracy (86.55%) than either SVM + LBP (85.43%) or SVM+GLCM (82.51%) for discriminating among four grade RA ultrasonic images. Overall, this methodology points to a significant grading of metacarpophalangeal RA ultrasound images without medical expert analysis or blood sample analysis, such as detecting C-reactive protein, measuring erythrocyte sedimentation rate, and testing rheumatoid factor.
Highlights
Rheumatoid arthritis (RA) is one of the most common autoimmune diseases, appearing in 0.5%–1% of the world’s population
The three quantitative geometric parameters are combined with gray-level co-occurrence matrix (GLCM) statistic texture features to describe the ultrasonic image of metacarpophalangeal RA
This study has described a grading of metacarpophalangeal RA on ultrasound images based on support vector machine (SVM) and the different feature descriptors
Summary
Rheumatoid arthritis (RA) is one of the most common autoimmune diseases, appearing in 0.5%–1% of the world’s population. The texture features of synovium ultrasonic image are applied to metacarpophalangeal RA ultrasonic image classification using machine learning algorithms. This study proposes a grading method of metacarpophalangeal RA ultrasonic images for detecting and estimating the geometric and texture features of synovium thickening and bone erosion.
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