Automatic Segmentation of Extensor Tendon of the MCP Joint in Ultrasound Images

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Sultan, MS (Author) Other; Martins, N (Author) Other; Veiga, D (Author) Other; Ferreira, M (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 2: BIOIMAGING, Rome, Italy, February 21-23, 2016.

Pages: 71-76

3rd International Conference on Bioimaging, BIOIMAGING 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016

21 February 2016 through 23 February 2016

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00K-GXK

DOI: 10.5220/0005692500710076

Abstract (EN): 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 for 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.

Language: English

Type (Professor's evaluation): Scientific