Automatic classification of digestive organs in wireless capsule endoscopy videos

Lee, Jeong-Kyu; Oh, Junghwan; Shah, Subodh Kumar; Yuan, Xiaohui; Tang, Shou Jiang

doi:10.1145/1244002.1244230

Cited by 69 publications

(30 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the reference [48], the textural features captured by Gabor filters are exploited in the problem of duodenum discrimination. The locations of the esogastric junction, pylorus, and ileo-cecal valve are estimated with an algorithm based on MPEG-7 visual descriptors in [49], while the color change pattern is exploited to detect different organs in [50].…”

Section: F Classifiers For Detection Of Diverse Organsmentioning

confidence: 99%

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

Prashanth¹,

Vani²

2017

IJIGSP

View full text Add to dashboard Cite

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.

show abstract

Section: F Classifiers For Detection Of Diverse Organsmentioning

confidence: 99%

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

Prashanth¹,

Vani²

2017

IJIGSP

View full text Add to dashboard Cite

show abstract

“…In [8], Lee at al. propose an algorithm to detect event boundaries based on the energy function in the frequency domain.…”

Section: Related Workmentioning

confidence: 99%

Sudden Changes Detection in WCE Video

Gallo¹,

Granata²,

Scarpulla

2009

Image Analysis and Processing – ICIAP 2009

View full text Add to dashboard Cite

Abstract. The direct visual inspection of WCE video by an expert is a tiring and cost activity and it is a true bottleneck to the widespread application of this diagnostic technique. In this paper we apply the texton approach to characterize with a numeric indicator the sub-sequences of a WCE that show sharp change and that are likely to represent relevant medical details. Experiments show that the proposed fully automatic technique may safely reduce the amount of frames that need further examination of up to 70%.

show abstract

“…Concept for multispectral tissue classification. After multispectral image acquisition, noise is removed and the resulting image is cropped into patches (1). From each of these patches the local binary pattern texture feature and the average spectrum are calculated (2) and fed into a support vector machine model to classify the organ characterized by the patch under investigation (3).…”

Section: Introductionmentioning

confidence: 99%

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

et al. 2016

View full text Add to dashboard Cite

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computerassisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

show abstract

Automatic classification of digestive organs in wireless capsule endoscopy videos

Cited by 69 publications

References 13 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

Sudden Changes Detection in WCE Video

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

Contact Info

Product

Resources

About