Blood detection in wireless capsule endoscopy using expectation maximization clustering

Hwang, Sae; Oh, Junghwan; Cox, Jay; Tang, Shou Jiang; Tibbals, H.F.

doi:10.1117/12.654109

Cited by 49 publications

(24 citation statements)

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“…Given M2A newest software provides a new feature for detection of luminal blood called Suspected Blood Indicator (SBI); achieving a sensitivity of 72% and specificity of 85%. The performance of SBI performance was improved through Bayesian In-formation Criterion (BIC) and Expectation Maximization (EM) clustering algorithm [9]; which was used to train probability models for the bleeding pixels and non-bleeding pixels with a sensitivity of 92% and specificity of 98% [38]. While Barbara Penna et al, have proposed blood detection algorithm based on color modeling, edge masking and Reed-Xiaoli RX detection [39,40].…”

Section: Classifiers For Detection Of Bleedingmentioning

confidence: 99%

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Prashanth¹,

Vani²

2017

IJIGSP

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Abstract-Wireless Capsule Endoscopy (WCE) is one of the methods for examination of gastrointestinal (GI) disorders such as obscure GI bleeding, Crohns disease, polyps etc. WCE has been recognized as a less expensive and painless procedure for the diagnosis of GI tract. This paper examines the various image classifiers designed and developed for the purpose of endoscopy focusing specifically on WCE. It is revealed that designing a suitable image classifier is an important prerequisite for accurate and precise diagnosis of malignancy in WCE. The assessment on various image classifiers used for the diagnosis of pathologies in different parts of GI tract shows that classifiers have reduced the diagnosis time for medical experts and also provided reasonably accurate diagnosis of malignancy. However, correlating classifiers and related pathologies is still observed to be challenging. In view of the fact that early detection may decrease the mortality rate significantly, inclination towards computer aided diagnosis are expected to increase in future. There is a need for advanced research in the development of a robust computer aided diagnosis system, capable of diagnosis of various pathologies in GI tract with higher degree of accuracy and reliability. Further, the study depicts that a direct comparison of results of classifier such as accuracy, prediction, sensitivity, specificity and precision to evaluate its performance is challenging due to diversity of image databases. More research is needed to identify and reduce the uncertainties in the application of image classifier to improve the diagnosis accuracy.

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Section: Classifiers For Detection Of Bleedingmentioning

confidence: 99%

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Prashanth¹,

Vani²

2017

IJIGSP

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“…In [9], the authors used clotted blood for bleeding. Although their technique aims to detect bleeding regions, it eventually detects blood regions.…”

Section: Erythemamentioning

confidence: 99%

“…However, all methods mentioned above are limited in viewing small intestine. To address the problem, Wire-less Capsule Endoscopy (WCE) was ¯first proposed in 2000, which integrates wireless transmission with image and video technology [6,9,14,15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Detection of Bleeding in Wireless Capsule Endoscopy Images Using Range Ratio Color

Al‐Rahayfeh¹,

Abuzneid²

2010

IJMA

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“…This scenario could also increase the chances of mistakes and misdiagnosis as physicians focus on a small part of the field for a long time. Hwang et al [15] applied expectation maximization clustering on a dataset of approximately 15,000 capsule endoscopy images for blood detection to overcome the drawbacks of traditional endoscopy. Tjoa and Krishnan [16] proposed a polyp detection technique utilizing the Texture Spectrum and a Neural Network classifier.…”

Section: Introductionmentioning

confidence: 99%

Stomach Tumor Localization Method of a Support Vector Machine Based on Capsule Endoscopy

Gong¹,

Zhang²

2017

PIER B

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Abstract-This study proposes a real-time method to solve the electromagnetic inverse scattering problem. This technique converts this problem into a regression problem using a support vector machine (SVM). The SVM-based solution successfully deals with the nonlinearity and ill-posedness inherent in this problem. Simulation results show the feasibility and effectiveness of the proposed method. The method can effectively locate the tumor target of the stomach regardless of the presence of noise. The positioning effect of the method improves as SNR increases. When the SNR is higher than 50 dB, noise minimally affects the results. Finally, the SVM prediction model is utilized to study the effect of the number of sampling locations on the prediction results. The results show that the more sampling locations, the better the prediction results.

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Blood detection in wireless capsule endoscopy using expectation maximization clustering

Cited by 49 publications

References 0 publications

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Detection of Bleeding in Wireless Capsule Endoscopy Images Using Range Ratio Color

Stomach Tumor Localization Method of a Support Vector Machine Based on Capsule Endoscopy

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