Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding

Prasath, V. B. Surya; Delhibabu, Radhakrishnan

doi:10.1007/978-81-322-2734-2_10

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Separating mucosal tissue and lumen (the area in which the capsule moves) regions is another important preprocessing step required to characterize features on mucosa rather than the lumen [4,5].…”

Section: Mucosa-lumen Separationmentioning

confidence: 99%

Automatic Image and Video Analysis for Capsule Endoscopy - An Open Frontier

Surya¹

2018

Int J Robot Eng

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Section: Mucosa-lumen Separationmentioning

confidence: 99%

Automatic Image and Video Analysis for Capsule Endoscopy - An Open Frontier

Surya¹

2018

Int J Robot Eng

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“…Table 2 summarizes the methods available for polyp localization and segmentation so far. A preliminary mucosa segmentation [30,[65][66][67] may be required before applying the polyp segmentation step in order to avoid these non-polyp pixels. …”

Section: Accurate Boundaries Segmentationmentioning

confidence: 99%

“…trash, illumination artifacts. A preliminary mucosa segmentation [60,27,61,62] maybe required before applying polyp segmentation step to avoid these non-polyp pixels.…”

Section: Ref Techniquementioning

confidence: 99%

Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

Prasath

2016

J. Imaging

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Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams, automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.

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“…Entropy theory is an important approach that is widely used for solving the edge detection problem: Zhen et al [11] combined grey entropy theory and textural features to detect the edge, Sert and Avci [20] used a technique that is called a neutrosophy based on maximum norm entropy. Recently, Abdel-Azim et al [1] proposed an edge detection algorithm based on non-parametric Fisher information (FI) measure [3], [4] based local thresholding value selection and masks. Promising results were obtained on edge detection of natural images when compared to edge detectors from the past.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection

et al. 2020

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Image edge detection is an important task in image processing and pattern recognition. Edges in digital images signify image discontinuities and traditionally gradient information is utilized in finding possible edge pixels. In this work, we consider a fusion approach using multiscale gradient maps along with non-parametric Fisher information which is recently used in edge detection. By using multiscale gradient maps we obtain better edge localization and robust edge maps and local thresholding with Fisher information helps obtain better detection. Experimental results on a variety of digital images and performance evaluation undertaken in comparison with edge detectors from the literature show the advantage of the proposed approach.

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Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding

Cited by 6 publications

References 8 publications

Automatic Image and Video Analysis for Capsule Endoscopy - An Open Frontier

Automatic Image and Video Analysis for Capsule Endoscopy - An Open Frontier

Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection

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