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Open AccessArticle

Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound

Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08873, USA
Author to whom correspondence should be addressed.
J. Imaging 2019, 5(4), 43;
Received: 5 March 2019 / Revised: 26 March 2019 / Accepted: 26 March 2019 / Published: 2 April 2019
Ultrasound (US) could become a standard of care imaging modality for the quantitative assessment of femoral cartilage thickness for the early diagnosis of knee osteoarthritis. However, low contrast, high levels of speckle noise, and various imaging artefacts hinder the analysis of collected data. Accurate, robust, and fully automatic US image-enhancement and cartilage-segmentation methods are needed in order to improve the widespread deployment of this imaging modality for knee-osteoarthritis diagnosis and monitoring. In this work, we propose a method based on local-phase-based image processing for automatic knee-cartilage image enhancement, segmentation, and thickness measurement. A local-phase feature-guided dynamic-programming approach is used for the fully automatic localization of knee-bone surfaces. The localized bone surfaces are used as seed points for automating the seed-guided segmentation of the cartilage. We evaluated the Random Walker (RW), watershed, and graph-cut-based segmentation methods from 200 scans obtained from ten healthy volunteers. Validation against manual expert segmentation achieved a mean dice similarity coefficient of 0.90, 0.86, and 0.84 for the RW, watershed, and graph-cut segmentation methods, respectively. Automatically segmented cartilage regions achieved 0.18 mm localization accuracy compared to manual expert thickness measurement. View Full-Text
Keywords: wltrasound; knee; osteoarthritis; segmentation; cartilage thickness; local phase wltrasound; knee; osteoarthritis; segmentation; cartilage thickness; local phase
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Desai, P.; Hacihaliloglu, I. Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound. J. Imaging 2019, 5, 43.

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