A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images

Kodogiannis, Vassilis; Boulougoura, M.; Lygouras, John N.; Petrounias, Ilias

doi:10.1016/j.neucom.2006.10.024

Cited by 44 publications

(22 citation statements)

References 19 publications

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“…Depending on the areas in gastrointestinal tract (GI), the methods can be broken down for the esophagus [16], the stomach [17,7], the small intestine [2][3][4][5] and the colon [9,18]. Depending on the specific lesions, the diagnosis methods can be classified to handle bleeding [2], cancer [19,17], Celiac disease, Helicobacter pylori [7], polyps [20,14] and ulcers [4], motility assessment [21], tumors [6,7], Barrett's esophagus, Crohn's disease [9,18], and just classify the region into normal and abnormal [22]. Some other applications include detecting informative frames [3], WCE color video segmentation [23], summarization [24] and clustering [25].…”

Section: Related Workmentioning

confidence: 99%

Deep sparse feature selection for computer aided endoscopy diagnosis

Cong

Wang

et al. 2015

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Deep sparse feature selection for computer aided endoscopy diagnosis

Cong

Wang

et al. 2015

Pattern Recognition

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“…However, there are few cases of its application to the endoscopic field except for the detection of dysplasia in patients with Barrett's esophagus and diagnosis via capsule endoscopy [14,15]. In the present study, by using a SVM, which is one of machine learning techniques available for high-precision discrimination, the diagnosability of gastric cancer using different endoscopy techniques was compared based on the color difference between the cancerous and non-cancerous areas.…”

Section: Introductionmentioning

confidence: 99%

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Ogawa

Nishikawa

Hideura

et al. 2018

J Gastrointest Canc

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Purpose The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences. Methods Eighteen histopathologically confirmed early GC lesions were examined. We prepared images from white light endoscopy (WL), indigo carmine (Indigo), and acetic acid-indigo carmine chromoendoscopy (AIM). A border between cancerous and non-cancerous areas on endoscopic images was established from post-treatment pathological findings, and 2000 pixels with equivalent luminance values were randomly extracted from each image of cancerous and non-cancerous areas. Each pixel was represented as a three-dimensional vector with RGB values and defined as a sample. We evaluated the Mahalanobis distance using RGB values, indicative of color differences between cancerous and non-cancerous areas. We then conducted diagnosis test using a support vector machine (SVM) for each image. SVM was trained using the 100 training samples per class and determined which area each of 1900 test samples per class came from. Results The means of the Mahalanobis distances for WL, Indigo, and AIM were 1.52, 1.32, and 2.53, respectively and there were no significant differences in the three modalities. Diagnosability per endoscopy technique was assessed using the F1 measure. The means of F1 measures for WL, Indigo, and AIM were 0.636, 0.618, and 0.687, respectively. AIM images were better than WL and Indigo images for the diagnosis of GC. Conclusion Objective assessment by SVM found AIM to be suitable for diagnosis of early GC based on color differences.

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“…Their approach takes into account the diagnostic information stored in the power spectrum of each category of images and through supervised learning separates characteristic feature vectors of each class in separate groups that helps assign a new test image to its respective group with around 80 % accuracy. Vassilis et al [9] have proposed a scheme that has been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from each color component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have also been adopted in their work.…”

Section: Introductionmentioning

confidence: 99%

Scene Classification Using Fuzzy Uncertainty Texture Spectrum

Rath

Mishra

Naik

2014

Advances in Intelligent Systems and Computing

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A method is proposed to discriminate rough and smooth scene images of various classes such as forest and coast, highway and inside city. The concept of fuzzy uncertainty texture spectrum (FUTS) is used. The fuzzy uncertainty parameter measures the uncertainty of the uniform surface in an image. Distribution of membership in a fuzzy image is called FUTS. The roughness or smoothness of a scene structure can be well explained by local texture information in a pixel and its neighborhood. Our proposed method uses the concept of FUTS to classify rough and smooth scene images. Probabilistic neural network (PNN) is used in the final stage to classify 150 images. This technique gives good result with less computational complexity.

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A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images

Cited by 44 publications

References 19 publications

Deep sparse feature selection for computer aided endoscopy diagnosis

Deep sparse feature selection for computer aided endoscopy diagnosis

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Scene Classification Using Fuzzy Uncertainty Texture Spectrum

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