Knee cartilage segmentation and thickness computation from ultrasound images

Faisal, Amir and Ng, Siew Cheok and Goh, Siew Li and Lai, Khin Wee (2018) Knee cartilage segmentation and thickness computation from ultrasound images. Medical & Biological Engineering & Computing, 56 (4). pp. 657-669. ISSN 0140-0118

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Official URL: https://doi.org/10.1007/s11517-017-1710-2

Abstract

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen’s κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.

Item Type: Article
Uncontrolled Keywords: Cartilage; Image segmentation; Knee joint; Level set; Thickness computation; Ultrasound
Subjects: R Medicine
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Aug 2019 03:32
Last Modified: 27 Aug 2019 03:32
URI: http://eprints.um.edu.my/id/eprint/22095

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