scholarly journals Organic Boundary Location Based on Color-Texture of Visual Perception in Wireless Capsule Endoscopy Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengliang Wang ◽  
Zhuo Luo ◽  
Xiaoqi Liu ◽  
Jianying Bai ◽  
Guobin Liao
Keyword(s):  
Visual Perception ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Support Vector ◽  
Svm Classifier ◽  
Phase Congruency ◽  
Region Detection ◽  
Wireless Capsule ◽  
Color Texture

This paper addresses the problem of automatically locating the boundary between the stomach and the small intestine (the pylorus) in wireless capsule endoscopy (WCE) video. For efficient image segmentation, the color-saliency region detection (CSD) method is developed for obtaining the potentially valid region of the frame (VROF). To improve the accuracy of locating the pylorus, we design the Monitor-Judge model. On the one hand, the color-texture fusion feature of visual perception (CTVP) is constructed by grey level cooccurrence matrix (GLCM) feature from the maximum moments of the phase congruency covariance and hue-saturation histogram feature in HSI color space. On the other hand, support vector machine (SVM) classifier with the CTVP feature is utilized to locate the pylorus. The experimental results on 30 real WCE videos demonstrate that the proposed location method outperforms the related valuable techniques.

scholarly journals Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Vahid Faghih Dinevari ◽  
Ghader Karimian Khosroshahi ◽  
Mina Zolfy Lighvan
Keyword(s):  
Singular Value Decomposition ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Singular Value ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Data Set ◽  
Value Decomposition ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

scholarly journals An Efficient Gastrointestinal Hemorrhage Detection and Diagnosis Model for Wireless Capsule Endoscopy

2019 ◽  
Vol 8 (3) ◽  
pp. 7549-7554 ◽  
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Classification Model ◽  
Support Vector ◽  
Frequency Spectra ◽  
Kernel Support Vector Machine ◽  
Detection And Diagnosis ◽  
New Feature ◽  
Occurrence Matrix ◽  
Wireless Capsule

Wireless Capsule Endoscopy (WCE) captures the section of human gastrointestinal (GI) tract which is impossible by the classical endoscopy investigations. A main limitation exist in the method is the requirement of analyzing massive data quantity for detecting the diseases which consumes more time and increases the burden to the physicians. As a result, there is a requirement to effectively develop an automated model to detect and diagnosis diseases on the WCEimages. The design of the presented model depends upon the examination of the patterns exist in frequency spectra of the WCE frames because of the occurrence of bleeding regions. For the exploration of the discriminating patterns,this study presents a new feature extraction based classification model is developed. An efficient Normalized Gray Level Co-occurrence Matrix (NGLCM) is applied for extracting the features of the GI images. Then, a kernel support vector machine (KSVM) with particle swarm optimization (PSO) is applied for the classification of the processed GI images. The experimentation takes place on the benchmark GI images to verify the superior nature of the presented model. The results confirmed the enhanced classifier outcome of the presented model on all the applied images under several aspects

scholarly journals Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 2688
Author(s):  
A. Al Mamun ◽  
P. P. Em ◽  
T. Ghosh ◽  
M. M. Hossain ◽  
M. G. Hasan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Capsule Endoscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Wireless Capsule Endoscopy ◽  
Innovative Technology ◽  
Support Vector ◽  
Bleeding Detection ◽  
Wireless Capsule

Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians.

scholarly journals An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Amit Kumar Kundu ◽  
Shaikh Anowarul Fattah ◽  
Mamshad Nayeem Rizve
Keyword(s):  
Capsule Endoscopy ◽  
Color Space ◽  
Intensity Variation ◽  
Human Perception ◽  
Wireless Capsule Endoscopy ◽  
Morphological Operations ◽  
Detection Scheme ◽  
Bleeding Detection ◽  
Rgb Color Space ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on interplane intensity variation profile in normalized RGB space. Next, from the extracted ROI, the variation in the normalized green plane is presented with the help of histogram. Features are extracted from the proposed normalized green plane histograms. For classification purpose, the K-nearest neighbors classifier is employed. Moreover, bleeding zones in a bleeding image are extracted utilizing some morphological operations. For performance evaluation, 2300 WCE images obtained from 30 publicly available WCE videos are used in a tenfold cross-validation scheme and the proposed method outperforms the reported four existing methods having an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%.

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