Computer Analysis of Knee by Magnetic Resonance Imaging Data

Suponenkovs, Artjoms; Markovičs, Zigurds; Platkājis, Ardis

doi:10.1016/j.procs.2017.01.145

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In [4], the research focused on the RoI of tibia texture for the analysis of OA. Texture analysis can be applied to various types of medical images, including X-ray [4], MRI [8], CT [9], and Infrared [10]. In addition, the texture of an object can be analyzed using texture descriptors, which is a technique to represent and handle texture in a numeric form.…”

Section: A Related Workmentioning

confidence: 99%

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Chan¹,

Dittakan²,

García-Constantino³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912.

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Section: A Related Workmentioning

confidence: 99%

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Chan¹,

Dittakan²,

García-Constantino³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The modified Outerbridge classification method (MOBC) 24,25 was considered as one of the established OA grading method. In this method, both the structural and biochemical characteristic changes of the cartilage were used for OA grade assessment.…”

Section: Introductionmentioning

confidence: 99%

A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

Thaha

Jogi

Rajan³

et al. 2021

Journal Orthopaedic Research

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To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2mapping, 2D-WearMap generation, and subregion analysis were performed semiautomatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computeraided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

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Image Segmentation for Detection of Knee Cartilage

Thengade¹,

Mutha²

2018

2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)

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Computer Analysis of Knee by Magnetic Resonance Imaging Data

Cited by 3 publications

References 6 publications

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

Image Segmentation for Detection of Knee Cartilage

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