Multiregion competition: A level set extension of region competition to multiple region image partitioning

Mansouri, Abdol-Reza; Mitiche, Amar; Vázquez, Carlos

doi:10.1016/j.cviu.2005.07.008

Cited by 109 publications

(85 citation statements)

References 37 publications

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“…Following the approach of Mansouri et al [7], each χ i is represented using the level set method as a function of R − 1 SDFs, φ . Simply, χ i is the region inside the zero contour (zero level set) of the i th SDF, and outside all previous SDFs.…”

Section: Region-based Energy Termmentioning

confidence: 99%

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Yazdanpanah¹,

Hamarneh

Smith

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis.

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Section: Region-based Energy Termmentioning

confidence: 99%

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Yazdanpanah¹,

Hamarneh

Smith

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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“…where the first two terms are the data terms of the region R i and R c i which are fully explained in [13], [14], the third term is the regularization term and the fourth term is our proposed prior term. ω i are the data in R i and ψ i are the data in R c i , for more details see [13], [14].…”

Section: Multi-level Set Methodsmentioning

confidence: 99%

“…ω i are the data in R i and ψ i are the data in R c i , for more details see [13], [14]. λ is positive real constant to weight the relative contribution of the energy equation.…”

Section: Multi-level Set Methodsmentioning

confidence: 99%

Medical Image Segmentation Using Multi-level Set Partitioning with Topological Graph Prior

Al-Shaikhli

Yang

Rosenhahn

2014

Image and Video Technology – PSIVT 2013 Workshops

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Abstract. In this paper, we propose an approach for multi-region segmentation based on a topological graph prior within a multi-level set (MLS) formulation. We consider topological graph prior information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level that would be difficult to segment correctly using standard methods. We describe our algorithm and show the graph prior technique to explain how it gives precise multi-region segmentation. We validate our algorithm with numerous abdominal and brain image databases and compare it to other multi-region segmentation methods to demonstrate its accuracy and computational efficiency.

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“…Gaussian models have been successfully used in many works on segmentation using natural images [31][32][33][34]. In our approach we also assume a Gaussian distribution of the region pixel values.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Unsupervised colour image segmentation by low-level perceptual grouping

Martínez-Usó

Pla

García-Sevilla

2011

Pattern Anal Applic

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This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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Multiregion competition: A level set extension of region competition to multiple region image partitioning

Cited by 109 publications

References 37 publications

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Medical Image Segmentation Using Multi-level Set Partitioning with Topological Graph Prior

Unsupervised colour image segmentation by low-level perceptual grouping

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