Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound

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. Ac...

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Vydané v:Journal of imaging Ročník 5; číslo 4; s. 43
Hlavní autori: Desai, Prajna, Hacihaliloglu, Ilker
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 02.04.2019
MDPI
Predmet:
Arthritis
Automation
Biomedical materials
Cartilage
cartilage thickness
Diagnosis
Image enhancement
Image processing
Image segmentation
Interfaces
Investigations
Knee
local phase
Localization
Magnetic resonance imaging
Methods
Noise
Osteoarthritis
segmentation
Thickness measurement
Ultrasonic imaging
wltrasound
ISSN:2313-433X, 2313-433X
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Shrnutí: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.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging5040043