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%.

Bleeding Detection in Wireless Capsule Endoscopy Using Color Similarity Coefficient

2012 ◽  
Vol 195-196 ◽  
pp. 307-312 ◽  
Author(s):  
Guo Bing Pan ◽  
Fang Xu ◽  
Jiao Liao Chen
Keyword(s):  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
The Novel ◽  
Similarity Coefficients ◽  
Bleeding Detection ◽  
Color Similarity ◽  
Color Vector ◽  
Wireless Capsule ◽  
Novel Algorithm

Wireless Capsule Endoscopy (WCE) generates a large number of images in one examination of a patient. It is very laborious and time-consuming to detect the WCE video, and limits the wider application of WCE. It is urgent and necessary to develop an automatic and intelligent computer aided bleeding detection technique. This paper proposes the color vector similarity coefficients to measure the color similarity, and based on which, a novel algorithm is implemented to recognize the bleeding in WCE images. The novel algorithm is implemented in RGB color space, and is featured with simple computation and practicability. The experiments show the sensitivity and specificity of this algorithm are 90% and 97% respectively.

scholarly journals DINOSARC: Color Features Based on Selective Aggregation of Chromatic Image Components for Wireless Capsule Endoscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michael D. Vasilakakis ◽  
Dimitris K. Iakovidis ◽  
Evaggelos Spyrou ◽  
Anastasios Koulaouzidis
Keyword(s):  
Capsule Endoscopy ◽  
Saliency Detection ◽  
Diagnostic Technique ◽  
Wireless Capsule Endoscopy ◽  
Salient Point ◽  
Detection Scheme ◽  
Level Information ◽  
Point Detection ◽  
Wireless Capsule ◽  
Selective Aggregation

Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software “stitches” the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors. Automating the WCE reading process could contribute in both the reduction of the examination time and the improvement of its diagnostic accuracy. In this paper, we present a novel feature extraction methodology for automated WCE image analysis. It aims at discriminating various kinds of abnormalities from the normal contents of WCE images, in a machine learning-based classification framework. The extraction of the proposed features involves an unsupervised color-based saliency detection scheme which, unlike current approaches, combines both point and region-level saliency information and the estimation of local and global image color descriptors. The salient point detection process involves estimation of DIstaNces On Selective Aggregation of chRomatic image Components (DINOSARC). The descriptors are extracted from superpixels by coevaluating both point and region-level information. The main conclusions of the experiments performed on a publicly available dataset of WCE images are (a) the proposed salient point detection scheme results in significantly less and more relevant salient points; (b) the proposed descriptors are more discriminative than relevant state-of-the-art descriptors, promising a wider adoption of the proposed approach for computer-aided diagnosis in WCE.

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