Statitical control of thinning algorithm with implementation based on hierarchical queues

Youssef, Rabaa; Sevestre, Patrick; Ricordeau, Anne

doi:10.1109/socpar.2014.7008034

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…They do not only describe the shape but also some mathematical properties of objects, such as length or surface [33][34][35]. The skeletons of the blood vessels are produced by a thinning technique [35][36][37][38]. In this study, the skeleton makes it possible to represent in a compact manner the properties of the blood vessels of the human retina in its particular shape.…”

Section: Retinal Vessel Centerline Detectionmentioning

confidence: 99%

Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation

Guedri¹,

Belmabrouk²

2020

Computer Methods and Programs in Biomedical Signal and Image Processing

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Fractal method is used in the image processing and studying the irregular and the complex shapes in the image. It is also used in the reconstruction and smoothing of one-, two-, and three-dimensional data. In this chapter, we present an interpolating fractal algorithm to reconstruct 3D blood vessels. Firstly, the proposed method determines the blood vessel centerline from the 2D retina image, and then it uses the Douglas-Peucker algorithm to detect the control points. Secondly, we use the 3D fractal interpolation and iterated function systems for the visualization and reconstruction of these blood vessels. The results showed that the obtained reduction rate is between 71 and 94% depending on the tolerance value. The 3D blood vessels model can be reconstructed efficiently by using the 3D fractal interpolation method.

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Section: Retinal Vessel Centerline Detectionmentioning

confidence: 99%

Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation

Guedri¹,

Belmabrouk²

2020

Computer Methods and Programs in Biomedical Signal and Image Processing

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“…Hence, this ensures the systematic examination of the darkest lowerable pixels and decreases the risk of disconnections. The operating mode we adopted has reduced the number of lowering instructions compared to Pink λ-Skeleton as described in [27].…”

Section: Implementing Hierarchical Queues For the Self Contrast Controlled Thinningmentioning

confidence: 99%

“…Second, we deduced that the lowering step in the thinning requires a decision that could be assimilated to a statistical hypothesis test. Therefore, a simple statistical test based on the range statistic was defined in [27] for the parametric thinning in order to choose the contrast parameter at the upper bound of the confidence interval. This chosen value allows to eliminate most of the noisy pixels and limits the lowering error rate.…”

Section: Introductionmentioning

confidence: 99%

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Self noise and contrast controlled thinning of gray images

Youssef

Sevestre

Ricordeau

et al. 2016

Pattern Recognition

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International audienceHomotopic grayscale thinning leads to " over-connected skeleton " when applied on noisy images. One way to avoid this phenomenon is the parametric thinning. It consists in relaxing the initial constraint by lowering low contrast crests, peaks and ends, according to a manually selected parameter and under the constraint of ascendant gray level processing. We propose to control this parameter by considering the lowering decision in a statistical framework of hypothesis test under the assumption of an additive Gaussian noise. A unitary hypothesis test based on the minimum test statistic is used for the elimination of peaks and noise related extremities, while a fusion of multiple tests is required for the insignificant crest lowering decision. This leads to a local adjustment and a standardization of the parametric thinning process that depends only on the chosen significance level of the test

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“…5. Results of gray skeletonization using methods published in [11] and in [12]. Color intensity (white to blue) reflects segments lengths.…”

Section: Linking Step For Segment Structurementioning

confidence: 99%

Graph Structuring of Skeleton Object for Its High-Level Exploitation

Youssef

Kacem²,

Sevestre³

et al. 2015

Lecture Notes in Computer Science

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Abstract. Skeletonization is a morphological operation that summarizes an object by its median lines while preserving the initial image topology. It provides features used in biometric for the matching process, as well as medical imaging for quantification of the bone microarchitecture. We develop a solution for the extraction of structural and morphometric features useful in biometric, character recognition and medical imaging. It aims at storing object descriptors in a re-usable and hierarchical format. We propose graph data structures to identify skeleton nodes and branches, link them and store their corresponding features. This graph structure allows us to generate CSV files for high level analysis and to propose a pruning method that removes spurious branches regarding their length and mean gray level. We illustrate manipulations of the skeleton graph structure on medical image dedicated to bone microarchitecture characterization.

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Statitical control of thinning algorithm with implementation based on hierarchical queues

Cited by 5 publications

References 10 publications

Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation

Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation

Self noise and contrast controlled thinning of gray images

Graph Structuring of Skeleton Object for Its High-Level Exploitation

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