Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N–D images

Zeng, Yun; Samaras, Dimitris; Chen, Wei; Peng, Qunsheng

doi:10.1016/j.cviu.2008.07.008

Cited by 76 publications

(62 citation statements)

References 19 publications

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“…We note that heuristics and approximation algorithms have been proposed for finding regions of various connectivity requirements [14,15]; however, to the best of our knowledge, our work is the first polynomial-time provably correct exact algorithm for such a problem.…”

Section: Introductionmentioning

confidence: 98%

“…However, with multiple seeds the cut might consist of several planar regions -for example, regions around the seeds, severing the blood vessel multiple times -instead of a desired single region. A natural solution to this problem is to enforce "contiguity" of the cut; similar concepts are known as a "connectivity prior" [14] and a "topology preserving cut" [6,15].…”

Section: Introductionmentioning

confidence: 99%

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Contiguous Minimum Single-Source-Multi-Sink Cuts in Weighted Planar Graphs

Bezáková

Langley

2012

Lecture Notes in Computer Science

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Abstract. We present a fast algorithm for uniform sampling of contiguous minimum cuts separating a source vertex from a set of sink vertices in a weighted undirected planar graph with n vertices embedded in the plane. The algorithm takes O(n) time per sample, after an initial O(n 3 ) preprocessing time during which the algorithm computes the number of all such contiguous minimum cuts. Contiguous cuts (that is, cuts where a naturally defined boundary around the cut set forms a simply connected planar region) have applications in computer vision and medical imaging [6,14].

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Contiguous Minimum Single-Source-Multi-Sink Cuts in Weighted Planar Graphs

Bezáková

Langley

2012

Lecture Notes in Computer Science

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“…As a benchmark, we compared our model with alternative models proposed by Zeng et al, [38] Boykov et al, [39] and Salazar-Gonzales et al [28] Unfortunately, it was not possible to test our method against a larger number of alternative methods because most of the published methods did not use a unique benchmark to measure the results of the optic disk segmentation, making a comparison of results difficult. Thus, comparisons of our proposed model were conducted using the topology cut, graph cut, compensation factor, and Markov random field (MRF) image reconstruction.…”

Section: Evaluation Experiments and Statistical Analysismentioning

confidence: 99%

“…In addition to ROC curves, accuracy was measured as a performance evaluator using the ratio of the number of correctly classified blood vessel pixels to the number of total pixels in the image. Topology cut [38] 0.5591 10.24…”

Section: Evaluation Experiments and Statistical Analysismentioning

confidence: 99%

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

Onal¹,

Dabil-Karacal²

2015

JBGC

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The retina can provide evidence of diseases originating in other parts of the body. Among the eye diseases that can be diagnosed through a retinal examination, age-related macular degeneration, glaucoma, and diabetic retinopathy are the most common, and they cause vision loss. Although these diseases can be diagnosed by examining blood vessels and the optic disk in retinal images, assessment of blood vessels on colored fundus images is a time-consuming and subjective process. Here, we present an automated blood vessel segmentation algorithm that facilitates the evaluation of diabetic retinopathy through assessment of blood vessel abnormalities. The blood vessels are extracted using a random forest classification model combined with wavelet features and local binary pattern texture information. Discriminant analysis is modified and used for feature selection to train the proposed classification model. The boundary of the optic disk is identified using low-pass filtering, fuzzy c-means clustering, and template matching so that it may be removed and not confound the segmentation analysis. Validation test results using three publicly available retinal image datasets demonstrated that our proposed method achieves as good or better blood vessel segmentation accuracy than the other supervised model approaches examined. Results show that the proposed scheme is able to segment the blood vessels and optic disk structures accurately (Accuracy Index) in 95.80%, 95.20%, and 97.10% of the testing Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and CHASE_DB1 image datasets respectively. The main advantage of our proposed model is that it provides robust and computationally efficient segmentation of blood vessels and the optic disk. The proposed model aims to provide supportive information for cases in which a diagnosis remains unclear following a clinical examination.

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“…The existing literature on region based image segmentation provides some ideas on how this can be accomplished for random field based models -in the form of so-called connectedness constraints. TopologyCuts is an extension of graphcuts and utilizes certain levelset ideas to preserve topology [41]. The DijkstraGC [38] finds a segmentation where two manually indicated seed points are connected via the foreground where as Nowozin [28] makes use of a LP relaxation.…”

Section: Problem Formulationmentioning

confidence: 99%

Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus

Collins

Singh

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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We study the problem of interactive segmentation and contour completion for multiple objects. The form of constraints our model incorporates are those coming from user scribbles (interior or exterior constraints) as well as information regarding the topology of the 2-D space after partitioning (number of closed contours desired). We discuss how concepts from discrete calculus and a simple identity using the Euler characteristic of a planar graph can be utilized to derive a practical algorithm for this problem. We also present specialized branch and bound methods for the case of single contour completion under such constraints. On an extensive dataset of ∼ 1000 images, our experiments suggest that a small amount of side knowledge can give strong improvements over fully unsupervised contour completion methods. We show that by interpreting user indications topologically, user effort is substantially reduced.

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Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N–D images

Cited by 76 publications

References 19 publications

Contiguous Minimum Single-Source-Multi-Sink Cuts in Weighted Planar Graphs

Contiguous Minimum Single-Source-Multi-Sink Cuts in Weighted Planar Graphs

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus

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