Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

Gornale, Shivanand S.; Patravali, Pooja U.; Uppin, Archana M.; Hiremath, P. S.

doi:10.5815/ijigsp.2019.02.06

Cited by 25 publications

(22 citation statements)

References 13 publications

(25 reference statements)

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“…Later, the implementation was carried out to automatically detect the region of interest using different segmentation methods. The methods like Sobel, Prewitt, Texture and Otsu"s methods were used and it is found that Prewitt method performed better with an accuracy of 97.55% compared to other segmentation methods [12]. Additionally, a novel pixel density based approach of automatically identifying the region of interest was developed that demonstrated a classification rate of 97.86% [13].…”

Section: Related Workmentioning

confidence: 99%

“…The pre-processing of knee X-ray images is performed for noise removal and detection of bone edges that are represented by salient shape features of a particular image. The knee radiography images contain prominently salt and pepper noise, which is filtered by using adaptive median filter that are beneficial in preserving edges of an image [12][13].…”

Section: B Pre-processingmentioning

confidence: 99%

“…Therefore, by defining the filter co-efficient as a sine wave, more weight is being added to the data in the middle of the image. Adaptive sine filter is used for allocating weights based on the geometrical axis of the reconstructed image [12]. Thus, the region of interest is accurately extracted, that can be further used for the medical examination [17].…”

Section: Identification and Extraction Of Region Of Interestmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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In this paper, the Osteoarthritis (OA) analysis in knee radiographic images using artificial neural networks (ANN) is considered. In Osteoarthritis, mobility is restricted and bones rub each other causing extreme pain in knee due to cartilage disintegration. The cartilage destruction is minimal in the initial stage of OA. It is observed that a small number of researchers have implemented identification and grading of Osteoarthritis utilizing their own datasets for experimentation. However, there is still need of automatic computer aided techniques to detect Osteoarthritis for early recognition. In this work, a dataset of 1650 radiographic images of knee joints of OA patients are collected from different hospitals and have been annotated by two different orthopedic surgeons as per the Kellgren and Lawrence (KL) grading system. To automate this grading procedure, the local phase quantization and multi-block projection profile features are computed from the images and then presented to artificial neural network to classify the images based on the KL grading of the severity of the disease. The classification accuracy of 98.7% and 98.2% with reference to surgeon-1 and surgeon-2 opinions, respectively, is achieved.

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Section: Related Workmentioning

confidence: 99%

Section: B Pre-processingmentioning

confidence: 99%

Section: Identification and Extraction Of Region Of Interestmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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“…K-Nearest Neighbor classifier: will classify the class label based on measuring the distance between testing and training data. KNN [46,48,49] will classify by suitable K value which in turn finds the nearest neighbor and provides a class label to un-labeled images. Depending on the types of problem, a variety of different distance measures can be implemented.…”

Section: Classifiersmentioning

confidence: 99%

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Patil¹,

Kruthi²,

Gornale³

2019

IJIGSP

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A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user's information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

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“…S. S. Gornale et al, have used contour based segmentation method for the analysis of OA using Knee x-ray images [11][12][13][14]. The semi-automated approach was utilized for extracting the region of interest.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

Gornale¹,

Patravali²

2019

IJIGSP

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Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

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Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

Cited by 25 publications

References 13 publications

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

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