Active Contour Segmentation based on Histograms and Dictionary Learning for Videocapsule Image Analysis

Gaetan Raynaud, Camille Simon-Chane, Pierre Jacob, Aymeric Histace

Abstract

This article deals with statistical region-based active contour segmentation using histograms and dictionary learning. Following previous publication, the active contour segmentation using optimization alpha-diver-gence family, leads to satisfying results. The method of the segmentation is based on histograms of the luminance of the pixels. To improve this method and to allow it to adapt to more types of images, we propose to replace luminance histograms with histograms of features using a bag of features model. This approach will be able to overcome the limitations of the luminance and give a better representation of the image. We will present the approach to create the new representation of the image, first with associated histograms to show its potential, using a local approach based on dictionary learning to compute the probability map of each pixel of the image to belong to the targeted object. In a second step using histograms based on bag of features for the representation of the image. We present experiments for the two methods on images extracted from small bowel videocapsule acquisitions and for two types of targeted objects (angiodysplasia and ulcer). We show that by replacing the luminance representation by a more complex one, we reach better performances for the segmentation of the targeted objects.

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Paper Citation


in Harvard Style

Raynaud G., Simon-Chane C., Jacob P. and Histace A. (2019). Active Contour Segmentation based on Histograms and Dictionary Learning for Videocapsule Image Analysis.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA, ISBN 978-989-758-354-4, pages 609-615. DOI: 10.5220/0007694706090615


in Bibtex Style

@conference{giana19,
author={Gaetan Raynaud and Camille Simon-Chane and Pierre Jacob and Aymeric Histace},
title={Active Contour Segmentation based on Histograms and Dictionary Learning for Videocapsule Image Analysis},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA,},
year={2019},
pages={609-615},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007694706090615},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA,
TI - Active Contour Segmentation based on Histograms and Dictionary Learning for Videocapsule Image Analysis
SN - 978-989-758-354-4
AU - Raynaud G.
AU - Simon-Chane C.
AU - Jacob P.
AU - Histace A.
PY - 2019
SP - 609
EP - 615
DO - 10.5220/0007694706090615