Random Walks for Image Segmentation

Grady, Leo

doi:10.1109/tpami.2006.233

Cited by 2,365 publications

(1,956 citation statements)

References 57 publications

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“…RW 8 , 15 is used to solve the segmentation problem by calculating the probability that a random walker starting at each voxel will first reach one of the labeled SPs. A graph consisting of a pair

G = (V, E)

with vertices (nodes)

v \in V

and edges

e \in E

is first created based on the image I to be segmented.…”

Section: Methodsmentioning

confidence: 99%

SPARSE: Seed Point Auto‐Generation for Random Walks Segmentation Enhancement in medical inhomogeneous targets delineation of morphological MR and CT images

Chen

Zhen

et al. 2015

J Applied Clin Med Phys

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In medical image processing, robust segmentation of inhomogeneous targets is a challenging problem. Because of the complexity and diversity in medical images, the commonly used semiautomatic segmentation algorithms usually fail in the segmentation of inhomogeneous objects. In this study, we propose a novel algorithm imbedded with a seed point autogeneration for random walks segmentation enhancement, namely SPARSE, for better segmentation of inhomogeneous objects. With a few user‐labeled points, SPARSE is able to generate extended seed points by estimating the probability of each voxel with respect to the labels. The random walks algorithm is then applied upon the extended seed points to achieve improved segmentation result. SPARSE is implemented under the compute unified device architecture (CUDA) programming environment on graphic processing unit (GPU) hardware platform. Quantitative evaluations are performed using clinical homogeneous and inhomogeneous cases. It is found that the SPARSE can greatly decrease the sensitiveness to initial seed points in terms of location and quantity, as well as the freedom of selecting parameters in edge weighting function. The evaluation results of SPARSE also demonstrate substantial improvements in accuracy and robustness to inhomogeneous target segmentation over the original random walks algorithm.PACS number: 87.57.nm

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G = (V, E)

with vertices (nodes)

v \in V

and edges

e \in E

is first created based on the image I to be segmented.…”

Section: Methodsmentioning

confidence: 99%

SPARSE: Seed Point Auto‐Generation for Random Walks Segmentation Enhancement in medical inhomogeneous targets delineation of morphological MR and CT images

Chen

Zhen

et al. 2015

J Applied Clin Med Phys

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show abstract

“…Each ellipse is characterized by its two axes, 'a'-the major and 'b'-the minor as shown in Fig. 8 [4,5]. The angle θ is defined by equation (3) [2, 9, 10].…”

Section: Mechanical Test On T-bone 1bmentioning

confidence: 99%

“…15 (grey scale of Keyence Image (A) in Fig. 16) [5,6]. The dark pixels in the image were identified either as cracks or voids, therefore the peak at the darker region were identified.…”

Section: Measurement Of Additional Featuresmentioning

confidence: 99%

“…17, many clusters and voids are noticed. The clusters are normally identified if the total volume of the detected contour is larger than the maximum possible area of the fiber [5,6]. Similar to voids, it is possible to quantify the location, distribution, size in addition the orientation of the clusters.…”

Section: Fig 15 Grey Scale Of the Total Image Used For Analysismentioning

confidence: 99%

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Complex fibers orientation distribution evaluation in short glass fiber-reinforced thermoplastic (PA66 GF50)

et al. 2018

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Abstract. The use of composites made of polyamide 6.6 matrix and short-glass-fibers in automobile industry is progressive due to its low density and cost. The injection molded short glass fiber-reinforced (SGFR) thermoplastics structural parts such as intake, manifold and engine mount housing, induce complex fiber orientation distributions (FOD). This microstructure governs the macroscopic properties such as the mechanical stiffness and fatigue resistance. To estimate the FOD on such industrial parts at complex angles and ribs, we rely on simulation results and micro tomography analysis. The interest of this paper is to develop a semi-automated, quick and efficient orientation tensor identification approach from 2D microscopic images, which is capable of observing relatively larger surface compared to micro tomography. We finally conclude by comparing it with micro tomography and simulation results. Furthermore, we investigate its relevance with fatigue service ability.

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“…All authors were with the Department of Computer and Information Science, University of Macau, Macau, China (e-mail: yb27405@umac.mo, cmpun@umac.mo). [7] in 2006, and then a large amount of Random Walks deformation one after another [8]- [10]. Moreover, Level Set is also another popular algorithms applied to interactive segmentation [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Robust Interactive Segmentation Using Color Histogram and Contourlet Transform

Huang¹,

Pun²

2015

IJCTE

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 Abstract-Efficient and accurate image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is hard to handle with natural images and texture images with complex background, thus interactive scheme with a few simple user inputs is a very good addition to image segmentation. For the purpose to accurately extract objects from different images, this paper presents a color histogram and Contourlet transform based interactive image segmentation. In the initialization stage, a superpixel based initial segmentation is applied to the original image. After that, the original image will be divided into a certain number of superpixels. Then, each superpixel will be represented by a novel superpixel feature based on color histogram and Contourlet transform. Finally, by using user-defined strokes, we merge the superpixels which are similar to marked object superpixels and merge the superpixels similar to marked background superpixels, until classify all superpixels. The computational complexity is analyzed, and comparative experimental results show that the proposed scheme can reliably and rapidly extract the desired object from the complex background.

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Random Walks for Image Segmentation

Cited by 2,365 publications

References 57 publications

SPARSE: Seed Point Auto‐Generation for Random Walks Segmentation Enhancement in medical inhomogeneous targets delineation of morphological MR and CT images

SPARSE: Seed Point Auto‐Generation for Random Walks Segmentation Enhancement in medical inhomogeneous targets delineation of morphological MR and CT images

Complex fibers orientation distribution evaluation in short glass fiber-reinforced thermoplastic (PA66 GF50)

Robust Interactive Segmentation Using Color Histogram and Contourlet Transform

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