Abstract
Rheumatoid Arthritis (RA) is a chronic inflammatory joint disease characterized by a distinctive pattern of bone and joint destruction. To give an RA diagnosis, hand bone radiographs are taken and analyzed. A hand bone radiograph analysis starts with the bone boundary detection. It is however an extremely exhausting and time consuming task for radiologists. An automatic bone boundary detection in hand radiographs is thus strongly required. Garcia et al. have proposed a method for automatic bone boundary detection in hand radiographs by using an adaptive snake method, but it doesn’t work for those affected by RA. The level set method has advantages over the snake method. It however often leads to either a complete breakdown or a premature termination of the curve evolution process, resulting in unsatisfactory results. For those reasons, we propose a modified level set method for detecting bone boundaries in hand radiographs affected by RA. Texture analysis is also applied for distinguishing the hand bones and other areas. Evaluating the experiments using a particular set of hand bone radiographs, the effectiveness of the proposed method has been proved.
Highlights
Rheumatoid Arthritis (RA) is a chronic and systemic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints
The boundaries of the hand bones firstly need to be detected for the hand bone radiograph analysis
Garcia et al [11] have proposed a fully automatic algorithm for detecting the boundaries of bones in hand radiographs by using an adaptive snake method
Summary
Rheumatoid Arthritis (RA) is a chronic and systemic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints. Garcia et al [11] have proposed a fully automatic algorithm for detecting the boundaries of bones in hand radiographs by using an adaptive snake method. This is because the speed function cannot properly detect the boundary and its detected boundary is dull even after filtering. The effectiveness of the proposed method is verified through the experiments by applying it to the real hand bone radiographs
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More From: International Journal of Advanced Computer Science and Applications
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