Identification of region of interest for assessment of knee osteoarthritis in radiographic images

Gornale, Shivanand S.; Patravali, Pooja U.; Hiremath, P. S.

doi:10.1504/ijmei.2021.10033611

Cited by 3 publications

(6 citation statements)

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“…A pixel density-based approach that recognized large radiographic pixel values as bone image pixels was applied to detect and extract the desired cartilage region [41,42]. Firstly, the computation was done using the HOG method and local binary pattern (LBP).…”

Section: Detection Of Knee Jointmentioning

confidence: 99%

“…Next, a decision tree classifier was used to classify the computed features. is approach achieved 97.86% and 97.61% accuracies with regard to the views of first and second medical experts [41]. After the cartilage detection, the resultant images were fed into an active contour algorithm to proceed with the segmentation process [42].…”

Section: Detection Of Knee Jointmentioning

confidence: 99%

“…Cheung et al [49] have tested the segmentation ability of four models, namely CUMed-Vision, U-Net, DeepLabv3, and Res-U-Net. All four models were used to segment distal (ii) Detected patellofemoral joints on X-ray images [44] (ii) Quantification of qualitative OA features (ii) Local binary pattern [41,42,44] (iii) Random forest regression voting [44] (iv) Fully convolutional neural network [45,46] (iii) Detected cartilage X-ray images [41,42] (v) YOLOv2 network [46] Segmentation of knee joint components Diagnosis (i) Segmented knee cartilage from 2D ultrasound images [27,28] (i) Area measurement (i) Locally statistical level set method [28] (ii) Segmented knee cartilage from 2D MRI images [47] (ii) Volumetric measurement (ii) Automatic seed point detection [48] (iii) Segmented cartilage and meniscus from MRI images [29] (iii) Joint shape measurement (iii) Random walker [27] (iv) Segmented subchondral bone from multiple 2D MRI images [48] (iv) Quantification of measurable OA features (iv) Watershed (v) Segmented distal femur and proximal tibia from X-ray images [49] (v) Reconstruction of 3D knee joint model for simulation and joint loading study (v) Graph cut [27] (vi) Calculated joint space width on X-ray images [49] (vi) Finite element analysis (vi) Support vector machine classifier [43] (vii) Segmented femoral condyle cartilage from ultrasound images [50] (vii) Utilization of statistical and computational models (vii) Decision tree classifier [41,42] (viii) Segmented bones (femur and tibia) and cartilages (femoral and tibial cartilages) on MRI images [51] (viii) Active contour algorithm [42] (ix) Segmented knee bones, cartilage, and muscle tissues on MRI images [52,53] (ix) U-Net [29,47,…”

Section: Segmentation Of Knee Jointmentioning

confidence: 99%

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Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

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Section: Detection Of Knee Jointmentioning

confidence: 99%

Section: Detection Of Knee Jointmentioning

confidence: 99%

Section: Segmentation Of Knee Jointmentioning

confidence: 99%

See 1 more Smart Citation

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…Gornale et al ( 2016b , c , 2017 , 2019a , b , c , 2020a , b ) have used the semi-automated active contour method for the extraction of the cartilage region and experimented with their own database of 500 Knee X-ray images. The different statistical features, geometric features and Zernike moments are computed and classified obtaining the accuracy of 87.92% for random forest classifier and 88.88% for K-NN classifier (Gornale et al, 2016b , c ).…”

Section: Related Workmentioning

confidence: 99%

“…The basic mathematical features are computed and classified giving an accuracy of 97.55% (Gornale et al, 2019a ). Further, a novel approach to identify and extract the region of interest based on the density of pixels is implemented in (Gornale et al, 2020a ). The extracted region is then used for computation using a histogram of the oriented gradient method and local binary pattern, the experimentation being done on a dataset of 1,173 Knee X-ray images.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

2020

Self Cite

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Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu's moments are helpful in extracting information from such distorted images due to their unique invariance property. This work focuses on early detection and gradation of Knee Osteoarthritis utilizing Hu's invariant moments to understand the geometric transformation of the cartilage region in Knee X-ray images. The seven invariant moments are computed for the rotated version of the test image. The results demonstrated are found to be more competitive and promising, which are validated by ortho surgeons and rheumatologists.

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Optimal knee osteoarthritis diagnosis using hybrid deep belief network based on Salp swarm optimization method

Balasubramanian¹,

Kishore

Devi³

2022

Concurrency and Computation

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Osteoarthritis (OA) damages the articular cartilage of the knee and is a severe degenerative joint condition. Currently, OA diagnosis is carried out by symptom analysis and progressive evaluation of the radiographs, although the method is subjective. This work presents a novel computer aided diagnostic system to diagnose the severity of the disease in its early stages. The proposed method comprises of stages that include image pre-processing, extraction of features based on discrete wavelet decomposition, histogram, GLCM and texture features, and classification by stacked up-RBM Deep Belief Networks (Hybrid DBN) finally. The performance of HDBN is improved by optimizing the hyper parameters with the Salp Swarm Optimization Algorithm (SSA). Experiments conducted on the Kaggle database consisting of 7503 images demonstrated an overall accuracy of 99.45% with 100% sensitivity and specificity. The impact on isolated and combined feature contributions is also analyzed using 10-fold cross validation (CV).The robustness of the algorithm is tested on degraded images of salt-pepper, Gaussian, and Poisson noise, which proved the effectiveness of the method. Comparison of the method proposed with similar techniques is done by employing different optimization algorithms and classifiers.

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Identification of region of interest for assessment of knee osteoarthritis in radiographic images

Cited by 3 publications

References 0 publications

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

Optimal knee osteoarthritis diagnosis using hybrid deep belief network based on Salp swarm optimization method

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