A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance

Bernal, Jorge; Sánchez, José; Vilariño, Fernando

doi:10.1007/978-3-642-21257-4_17

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…Studies [18][19][20] address the removal of the effects of interlacing through deinterlacing. The procedure is based on obtaining one in every two horizontal lines, decreasing the vertical…”

Section: Removal Of Interlacing Effectsmentioning

confidence: 99%

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

Sánchez-González¹,

Soto²

2018

Surgical Robotics

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Colorrectal cancer is the third most frequently diagnosed cancer worldwide. The American Cancer Society estimates that there will be almost 100,000 new patients diagnosed with colorectal cancer and that around 50,000 people will die as a consequence of this in 2016. The increase of life expectancy and the increment of the number of diagnostic tests conducted have had a great impact on the amount of cancers being detected. Among other diagnostic tools, colonoscopy is the most prevalent. In order to help endoscopists cope with the increasing amount of tests that have to be carried out, there exists a need to develop automated tools that aid diagnosis. The characteristics of the colon make preprocessing essential to eliminate artefacts that degrade the quality of exploratory images. The goal of this chapter is to describe the most common issues of colonoscopic imagery as well the existing methods for their optimal detection and correction.

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Section: Removal Of Interlacing Effectsmentioning

confidence: 99%

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

Sánchez-González¹,

Soto²

2018

Surgical Robotics

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“…Collins et al [15] proposed a method using Conditional Random Field (CRF) with Deformable Parts Model (DPM) and a response of positive Laplacian of Gaussian filter (negative responses clipped to zero). Some other methods using watershed and region merging [16] or GrabCut [17] have proposed.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Subtree Selection for NBI Endoscopic Image Labeling

Hirakawa

Tamaki

Kurita

et al. 2017

Computer Vision – ACCV 2016 Workshops

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Abstract. In this paper, we propose a novel method for image labeling of colorectal Narrow Band Imaging (NBI) endoscopic images based on a tree of shapes. Labeling results could be obtained by simply classifying histogram features of all nodes in a tree of shapes, however, satisfactory results are difficult to obtain because histogram features of small nodes are not enough discriminative. To obtain discriminative subtrees, we propose a method that optimally selects discriminative subtrees. We model an objective function that includes the parameters of a classifier and a threshold to select subtrees. Then labeling is done by mapping the classification results of nodes of the subtrees to those corresponding image regions. Experimental results on a dataset of 63 NBI endoscopic images show that the proposed method performs qualitatively and quantitatively much better than existing methods.

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“…As specular highlights have an impact in the valleys detection [2] we mitigate their effect by approximating what should really should be under them, by substituting the affected pixels by a linear combination of the values around them. The valley detector used to obtain the DV image needs of two parameters, differentiation and integration sigma [8].…”

Section: Fig 1 Definition Of the Depth Of Valleys Imagementioning

confidence: 99%

“…As we mentioned in Section 1, we will use as the region segmentation stage the results provided in [2], which provides, for each image, a reduced number of regions one of them containing a polyp. In the case of region description, we will use both EF-DOVA [3] and the novel VO-DOVA.…”

Section: Fig 4 Polyp Examples (A) Flat (B) Peduncular (C) Lateral Vmentioning

confidence: 99%

“…Intensity valleys appear to surround polyps as the light of the colonoscope and the camera are in the same direction, and for this reason, they are proposed as a cue to detect polyps by means of computer vision techniques [2]. In this paper we present a novel region descriptor built on the concept of the depth of valleys image (DoV image), which combines valley localization given by a valleys detector with the intensity information provided by morphological gradient.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy

Bernal

Sánchez

Vilariño

2012

Lecture Notes in Computer Science

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Abstract. This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed description method consists of defining, for each point, a series of radial sectors around it and then accumulate the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming another approach that also integrate depth of valleys information.

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A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance

Cited by 14 publications

References 7 publications

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

Discriminative Subtree Selection for NBI Endoscopic Image Labeling

Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy

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