Segmentation of Bones & MCP Joint Region of the Hand from Ultrasound Images

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

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

Conference proceedings International

Title: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Pages: 3001-3004

37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Milan, ITALY, AUG 25-29, 2015

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00K-2N2

DOI: 10.1109/embc.2015.7319023

Abstract (EN): Rheumatic arthritis (RA) is an autoimmune disease that causes irreversible damage to joints and other physiological structures. The Metacarpophalangeal (MCP) joint is one of the first regions to suffer alterations. These alterations are visible with high frequency ultrasound devices, which are used to quantify inflammatory activity in the MCP due to RA. The accurate segmentation of the bone surface and the identification of the MCP capsule region remains a challenge in ultrasound image processing. In this article we aim to make a contribution to this problem by incorporating prior knowledge of the bone and joint regions anatomy into our segmentation algorithm. The log Gabor filter is used for speckle noise reduction and to extract ridge-like structures from the images, while the phase is left unchanged. After thresholding, scores are generated, based on the intensities and areas of the resulting regions, enabling the selection of the structure that best matches the bone. Finally, segmented joint bones are processed to calculate the initial seeds of joint capsule region. Experimental results demonstrate the accuracy of the proposed segmentation algorithm. The mean pixel error between the automatic segmentation and the reference images were 4.4 pixel. The bone regions not segmented were, on average, 5.4%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4