Authors:
Malik Saad Sultan
1
;
Nelson Martins
2
;
Diana Veiga
3
;
Manuel Ferreira
3
and
Miguel Coimbra
1
Affiliations:
1
Instituto de Telecomunicações, Faculdade de Ciências and Universidade do Porto, Portugal
;
2
Instituto de Telecomunicações, Enermeter, Sistemas de Medição and Lda, Portugal
;
3
Enermeter, Sistemas de Medição, Lda and University of Minho, Portugal
Keyword(s):
Rheumatoid Arthritis, Ultrasound, Tendon Segmentation, Log-Gabor Filter, MCP joint.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Image Processing Methods
;
Medical Imaging and Diagnosis
;
Ultrasound and Optical Imaging
Abstract:
Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the small joints of the hand. High frequency ultrasound imaging is used to measure the inflammatory activity in the joint capsule region of Metacarpophalangeal (MCP) joint. In our previous work, the problem of bones and joint capsule segmentation was addressed and in this work we aim to automatically identify the tendon using previously segmented structures. The extensor tendon is located above the metacarpal and phalange bone and the joint capsule. Tendon and bursal involvement are frequent and often clinically dominant in early RA. Ridge-like structures are enhanced and pre-processed to reduce speckle noise using a Log-Gabor filter. These regions are then simplified using medial axis transform and vertically connected lines are removed. Adjacent lines are connected using morphological operators and short lines are filtered by thresholding. Physiological information is used to create a distance map fo
r all the lines using prior knowledge of the bone and capsule region location. Based on this distance map, the tendon is finally segmented and its shape refined by using active contours. The segmentation algorithm was tested on 90 images and experimental results demonstrate the accuracy of the proposed algorithm. The automatic segmentation was compared with an expert manual segmentation, and a mean error of 3.7 pixels and a standard deviation of 2 pixels were achieved, which are interested results for integration into future computer-assisted decision systems.
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