Towards more adequate colour histograms for in-body images

Sousa, Abel; Dinis-Ribeiro, Mário; Areia, Miguel; Correia, Miguel Velhote; Coimbra, Miguel

doi:10.1109/iembs.2008.4649630

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…They do not expend the full spectrum of the color space. Using the equally spaced histogram bin will lead to under and over-usage of the HSV histogram bins [7]. For this reason, in our study, we have focused on creating histogram bins which can maximize and adapt the usage of all the available color specifically for WCE frames.…”

Section: ) Adaptive Color Histogrammentioning

confidence: 99%

Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

Poh

Htwe

et al. 2010

2010 IEEE Conference on Cybernetics and Intelligent Systems

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This paper presents a novel multi-level approach for bleeding detection in Wireless Capsule Endoscopy (WCE) images. In the low-level processing, each cell of K×K pixels is characterized by an adaptive color histogram which optimizes the information representation for WCE images. A Neural Network (NN) cell-classifier is trained to classify cells in an image as bleeding or non-bleeding patches. In the intermediate-level processing, a block which covers 3×3 cells is formed. The intermediate-level representation of the block is generated from the low-level classifications of the cells, which captures the spatial local correlations of the cell classifications. Again, a NN blockclassifier is trained to classify the blocks as bleeding or nonbleeding ones. In the high-level processing, the low-level cellbased and intermediate-level block-based classifications are fused for final detection. In this way, our approach can combine the low-level features from pixels and intermediate-level features from local regions to achieve robust bleeding detection.Experiments on real WCE videos have shown that the proposed method of multi-level classification is not only accurate in both detection and localization of potential bleedings in WCE images but also robust to complex local noisy features.

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Section: ) Adaptive Color Histogrammentioning

confidence: 99%

Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

Poh

Htwe

et al. 2010

2010 IEEE Conference on Cybernetics and Intelligent Systems

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IDentifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms

Sousa

Dinis-Ribeiro

Areia

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Quantitative comparison of segmentation methods for in-body images

Riaz

Dinis-Ribeiro

Coimbra

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, we present a numerical comparison of how well segmentation algorithms approximate the manual segmentation of gastroenterologists for a set of endoscopic images. Different areas in these images demand different levels of analysis by a clinician and some provide critical information about the patient. Our objective is thus to segment endoscopic images so that the results mimic as closely as possible the areas that were considered relevant by doctors. We focus on a detailed quantitative comparison of two popular segmentation algorithms, mean shift and normalized cuts, when applied to in-body images, most specifically for vital-stained magnification endoscopy. Segmentation results are compared with the manual annotations of the same images performed by two specialist clinicians. Results show that if we simply consider the most relevant segmented patch, normalized cuts performs better. However, if we allow the annotated area to be represented by multiple patches, mean shift is clearly a better choice, although automatic ways to determine its kernel's bandwidth are highly desirable.

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Towards more adequate colour histograms for in-body images

Cited by 4 publications

References 10 publications

Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

IDentifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms

Quantitative comparison of segmentation methods for in-body images

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