A logic framework for active contours on multi-channel images

Sandberg, Berta; Chan, Tony F.

doi:10.1016/j.jvcir.2004.08.005

Cited by 28 publications

(18 citation statements)

References 16 publications

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“…A generalization of the active contour without edges model was proposed in [23] for object detection using logic operations. This logic framework suffers from the same limits as in the active contour model and is not suitable for detecting tubular structures.…”

Section: Introductionmentioning

confidence: 99%

Framelet-Based Algorithm for Segmentation of Tubular Structures

Cai

Chan

Morigi

et al. 2012

Lecture Notes in Computer Science

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Abstract. Framelets have been used successfully in various problems in image processing, including inpainting, impulse noise removal, superresolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the partial differential equation modeling. In this paper, we apply the framelet-based approach to identify tube-like structures such as blood vessels in medical images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the framelet-based algorithm to denoise and smooth the possible boundary and sharpen the region. Numerical experiments of real 2D/3D images demonstrate that the proposed method is very efficient and outperforms other existing methods.

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Section: Introductionmentioning

confidence: 99%

Framelet-Based Algorithm for Segmentation of Tubular Structures

Cai

Chan

Morigi

et al. 2012

Lecture Notes in Computer Science

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“…This emerging trend in the area of texture segmentation has been reinforced by the vector formulation of recent active contour approaches Sandberg and Chan, 2005) introduced to provide a natural platform for the embedment of textural features. Such methods constitute an essential first step in computer vision applications, which are as diverse as medical image analysis, industrial monitoring of product quality, content-based image retrieval and remote sensing.…”

Section: Introductionmentioning

confidence: 99%

LBP-guided active contours

Savelonas

Iakovidis

Maroulis

2008

Pattern Recognition Letters

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This paper investigates novel LBP-guided active contour approaches to texture segmentation. The Local Binary Pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, IntroductionTexture segmentation methods based on active contour approaches have received considerable attention over the past few years (Theodoridis and Koutroumbas, 2006;Paragios and Deriche, 1999;Lehmann et al, 2001;Aujol et al, 2003;Rousson et al, 2003;Sagiv et al, 2004;Huang et al, 2004;He et al, 2004;Allili et al, 2004;Pujol and Radeva, 2004;Lee et al, 2005), by exploiting advances in the active contour research such as contour smoothness, noise robustness and topological adaptability. This emerging trend in the area of texture segmentation has been reinforced by the vector formulation of recent active contour approaches Sandberg and Chan, 2005) introduced to provide a natural platform for the embedment of textural features. Such methods constitute an essential first step in computer vision applications, which are as diverse as medical image analysis, industrial monitoring of product quality, content-based image retrieval and remote sensing.The main notion of the active contour approach to texture segmentation relies on the deformation of initial contours towards the boundaries of image regions to be segmented. The deformation is realized by minimizing an energy functional, designed so that its local minimum is reached at target boundaries. Active contour models lead to

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“…Instead of one registration map as in [16,17], two affine mappings for registration are used for better performance in terms of locating the minimum of the variational energy. The energy to be minimized is constructed with logic models that are taken from Sandberg and Chan [12]. This is somewhat similar to the multichannel algorithm by Vese and Chan [15].…”

mentioning

confidence: 99%

“…The logic models, developed by Sandberg and Chan [12], are designed to segment multichannel images according to logical combination of the two images. Each logical relation and interpretation of the two images may correspond to a different minimum in the multi-channel algorithm.…”

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confidence: 99%

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Registration-Based Morphing of Active Contours for Segmentation of CT Scans

Young

Levy

2005

Mathematical Biosciences and Engineering

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Abstract. We present a new algorithm for segmenting organs in CT scans for radiotherapy treatment planning. Given a contour of an organ that is segmented in one image, our algorithm proceeds to segment contours that identify the same organ in the consecutive images. Our technique combines partial differential equations-based morphing active contours with algorithms for joint segmentation and registration. The coupling between these different techniques is done in order to deal with the complexity of segmenting "real" images, where boundaries are not always well defined, and the initial contour is not an isophote of the image.

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A logic framework for active contours on multi-channel images

Cited by 28 publications

References 16 publications

Framelet-Based Algorithm for Segmentation of Tubular Structures

Framelet-Based Algorithm for Segmentation of Tubular Structures

LBP-guided active contours

Registration-Based Morphing of Active Contours for Segmentation of CT Scans

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