Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images

Koh, Joel En Wei; Hagiwara, Yuki; Oh, Shu Lih; Tan, Jen Hong; Ciaccio, Edward J.; Green, Peter H.; Lewis, Suzanne K.; Acharya, U. Rajendra

doi:10.1016/j.future.2018.07.044

Cited by 26 publications

(11 citation statements)

References 35 publications

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“…Discussion. In previous studies, Koh et al [28] The results of our proposed methods seem slightly lower than the results reported by Wimmer et al [46].…”

Section: Discussion and Future Workcontrasting

confidence: 73%

“…Wavelet based methods are frequently used in the CD classification [5,6,[20][21][22][23][24][25][26][27][28] in the literature. In this study, father Daubechies orthogonal discrete wavelet transform (DWT) [29] was preferred for decompose images into subbands such as 3 different decomposition levels with Haar wavelet low and high pass filters.…”

Section: Context Based Image Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Saken¹,

Yagci²,

Yumuşak³

2021

Turk J Elec Eng & Comp Sci

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Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanent intolerance to gluten and other cereal proteins in cereals. It is considered as one of the most difficult diseases to diagnose. Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis. Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance the diagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methods have been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique was employed to segment the images. Afterward, images were decomposed into subbands with discrete wavelet transform (DWT), and the distinctive features were extracted with scale invariant texture recognition. Classification accuracy, sensitivity and specificity ratio are 94.79%, 94.29% and 95.08% respectively. The results of the proposed models are compared with the result of other state-of-the-art methods such as a convolutional neural network (CNN) and higher order spectral (HOS) analysis. The results showed that the proposed hybrid approaches are accurate, fast and robust.

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“…Discussion. In previous studies, Koh et al [28] The results of our proposed methods seem slightly lower than the results reported by Wimmer et al [46].…”

Section: Discussion and Future Workcontrasting

confidence: 73%

Section: Context Based Image Segmentationmentioning

confidence: 99%

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Saken¹,

Yagci²,

Yumuşak³

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…An SVM model trained on WCE images from 13 control and 13 celiac patients was able to identify celiac disease with a sensitivity of 88% and a specificity of 87%. 55 Another SVM model improved upon these results and was able to detect disease with sensitivity and specificity of 97% and 96%, respectively. 56 WCE also plays an important role in IBD, in particular, Crohn's disease as it allows for assessment of the entire small bowel.…”

Section: Wireless Capsule Endoscopymentioning

confidence: 94%

Artificial Intelligence and the Future of Gastroenterology and Hepatology

Penrice

Rattan

Simonetto

2022

Gastro Hep Advances

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“…Generally, there must be a connection between the pixels in the selected neighborhood, so when assigning the main direction to the feature points, it can be determined according to the distribution of the gradient direction of the pixels in the neighborhood [13,14]. The specific method is to take the selected feature points as the center and select a certain neighborhood block [15]. When the gradient direction statistics of all pixels in this block is based on histogram statistics, the gradient direction corresponding to the peak value is selected as the main direction of the characteristic point.…”

Section: Assignment Of Candidate Feature Directionsmentioning

confidence: 99%

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Yin

2022

Mobile Netw Appl

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In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

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Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images

Cited by 26 publications

References 35 publications

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Artificial Intelligence and the Future of Gastroenterology and Hepatology

Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

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