Computer‐based measurements of joint space analysis using metacarpal morphometry in hand radiograph for evaluation of rheumatoid arthritis

• Published in: International journal of rheumatic diseases Vol. 20; no. 9; pp. 1120 - 1131
• Main Authors: Snekhalatha, U., Anburajan, M.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.09.2017
• Subjects: Adult; Aged; Algorithms; Arthritis, Rheumatoid - blood; Arthritis, Rheumatoid - diagnostic imaging; Arthrography - methods; Automation; Biomarkers - blood; Blood Sedimentation; C-reactive protein; C-Reactive Protein - analysis; Case-Control Studies; cortical thickness; disease activity score; dual tree complex wavelet transform; Erythrocyte sedimentation rate; Female; Humans; Joint diseases; joint space width; Male; Metacarpal; Metacarpal Bones - diagnostic imaging; Metacarpophalangeal Joint - diagnostic imaging; Middle Aged; Morphometry; Population studies; Predictive Value of Tests; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography; Rheumatoid arthritis; Rheumatoid factor; Rheumatoid Factor - blood; Segmentation; Statistical analysis; Statistics; Surveys and Questionnaires; watershed algorithm; Watersheds; Wavelet Analysis; Wavelet transforms; dual tree complex wavelet transform; watershed algorithm; joint space width; rheumatoid arthritis; cortical thickness; disease activity score
• ISSN: 1756-1841; 1756-185X; 1756-185X
• DOI: 10.1111/1756-185X.12559

Aim and objectives The aim and objectives are as follows: (i) to perform an automated segmentation of the hand from radiographs using a dual tree complex wavelet‐based watershed algorithm; ii) to compare the measured statistical features of the joint space of the hand using gray level co‐occurrence matrix (GLCM) method with standard diagnostic parameters of rheumatoid arthritis (RA). Methods Fifty‐three patients with RA and 17 age‐ and sex‐matched healthy controls were included in the study. The erythrocyte sedimentation rate (ESR), C‐reactive protein, rheumatoid factor, health assessment questionnaire score (HAQ), disease activity score (DAS) and hand radiographs of all the subjects were obtained. Joint space width and cortical thickness were measured in metacarpophalangeal joints (MCP) and metacarpal bone semi‐automatically using MIMICS software. Dual tree complex wavelet transform‐based watershed algorithm was applied for automated segmentation, and feature extraction was performed using the GLCM method in hand radiographs of the total population. Results In the RA group (n = 53), the joint space width measured in the MCP1, MCP3, MCP5 of the hand were reduced significantly (P < 0.01) by 16.4%, 15.6%, and 17.5%, respectively compared to the normal group (n = 17). The measured combined cortical thickness at the second, third and fourth metacarpal bones of the hand were reduced significantly (P < 0.01) by 9.5%, 12% and 8%, respectively in the RA group compared to the normal group. Conclusion The dual tree complex wavelet transform‐based watershed algorithm provided effective segmentation in the digitized hand radiographs. The standard diagnostic parameters for RA were highly correlated with measured statistical features at MCP3 hand joint in the total population studied.