scholarly journals Small intestine bleeding detection using color threshold and morphological operation in WCE images

2021 ◽  
Vol 11 (4) ◽  
pp. 3040
Author(s):  
A. Al Mamun ◽  
M. S. Hossain ◽  
P. P. Em ◽  
A. Tahabilder ◽  
R. Sultana ◽  
...  
Keyword(s):  
Significant Contribution ◽  
Feature Vector ◽  
Color Space ◽  
Support Vector ◽  
Morphological Operation ◽  
Hsv Color Space ◽  
Non Invasive ◽  
Bleeding Detection ◽  
Computer Aided ◽  
Wireless Capsule

<span>Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. </span>

scholarly journals Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

2019 ◽  
Vol 9 (12) ◽  
pp. 2499
Author(s):  
Yiling Tang ◽  
Shunliang Jiang ◽  
Shaoping Xu ◽  
Tingyun Liu ◽  
Chongxi Li
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Support Vector ◽  
Feature Maps ◽  
Hsv Color Space ◽  
Window Method ◽  
Blind Image Quality Assessment ◽  
Blind Image

To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.

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%.

scholarly journals Intelligent Classification Technique for Breast Cancer

2019 ◽  
Vol 8 (6) ◽  
pp. 2313-2316
Keyword(s):  
Breast Cancer ◽  
Developed Countries ◽  
Invasive Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Image Processing Algorithm ◽  
Non Invasive ◽  
Computer Aided ◽  
Aided Diagnosis

Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.

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 Cloud Classification of Ground-Based Images Using Texture–Structure Features

2014 ◽  
Vol 31 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Wen Zhuo ◽  
Zhiguo Cao ◽  
Yang Xiao
Keyword(s):  
Feature Vector ◽  
Color Space ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Census Transform ◽  
Structure Information ◽  
Cloud Classification ◽  
Opponent Color

Abstract Cloud classification of ground-based images is a challenging task. Recent research has focused on extracting discriminative image features, which are mainly divided into two categories: 1) choosing appropriate texture features and 2) constructing structure features. However, simply using texture or structure features separately may not produce a high performance for cloud classification. In this paper, an algorithm is proposed that can capture both texture and structure information from a color sky image. The algorithm comprises three main stages. First, a preprocessing color census transform (CCT) is applied. The CCT contains two steps: converting red, green, and blue (RGB) values to opponent color space and applying census transform to each component. The CCT can capture texture and local structure information. Second, a novel automatic block assignment method is proposed that can capture global rough structure information. A histogram and image statistics are computed in every block and are concatenated to form a feature vector. Third, the feature vector is fed into a trained support vector machine (SVM) classifier to obtain the cloud type. The results show that this approach outperforms other existing cloud classification methods. In addition, several different color spaces were tested and the results show that the opponent color space is most suitable for cloud classification. Another comparison experiment on classifiers shows that the SVM classifier is more accurate than the k–nearest neighbor (k-NN) and neural networks classifiers.

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