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

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.