Seeded ND medical image segmentation by cellular automaton on GPU

Kauffmann, Claude; Piché, Nicolas

doi:10.1007/s11548-009-0392-0

Cited by 47 publications

(28 citation statements)

References 32 publications

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“…This is because liver region pixel intensity is sightly different from the surrounding muscles. There have been numerous efforts [24][25][26][27][28][29] to date to test and improve the efficiency of CA. However, most of the methods that include CA have been hand created ad hoc and, even though some do consider multidimensional images, they are still far from the dimensions of hyperspectral images and are usually projected onto a lower dimension during the segmentation process.…”

Section: Challengesmentioning

confidence: 99%

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

2015

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Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computerassisted interventions grand challenge workshop. Various parameters in the algorithm, such as w , Δt , z, α, μ, α 1 , and α 2 , play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

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Section: Challengesmentioning

confidence: 99%

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

2015

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“…During removal of film artifacts, the images will have salt and pepper noise. Histogram equalization is a spatial domain image enhancement technique that modifies the distribution of the pixels to become more evenly distributed over the available pixel range [8]. In histogram processing, a histogram displays the distribution of the pixel intensity value, mimicking in the probability density function (PDF) for a continuous function.…”

Section: Pre-processingmentioning

confidence: 99%

Automated Brain Tumor Segmentation Using Hsom

Monica¹

2013

IOSR-JECE

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“…Hussein et al [20] and Vineet et al [21] proposed a parallel version of graph cuts, Sharma et al [22] and Roberts et al [23] both introduced a version of a parallel level-set algorithm, Kauffmann et al [24] implemented a cellular automaton segmenter on GPGPUs, while Laborda et al [25] presented a real-time GPGPU-based segmenter using Gaussian mixture models.…”

Section: Acceleration Strategies Tested In Standard Definitionmentioning

confidence: 99%

High-resolution image segmentation using fully parallel mean shift

Varga

Karacs

2011

EURASIP J. Adv. Signal Process.

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In this paper, we present a fast and effective method of image segmentation. Our design follows the bottom-up approach: first, the image is decomposed by nonparametric clustering; then, similar classes are joined by a merging algorithm that uses color, and adjacency information to obtain consistent image content. The core of the segmenter is a parallel version of the mean shift algorithm that works simultaneously on multiple feature space kernels. Our system was implemented on a many-core GPGPU platform in order to observe the performance gain of the data parallel construction. Segmentation accuracy has been evaluated on a public benchmark and has proven to perform well among other data-driven algorithms. Numerical analysis confirmed that the segmentation speed of the parallel algorithm improves as the number of utilized processors is increased, which indicates the scalability of the scheme. This improvement was also observed on real life, high-resolution images.

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Seeded ND medical image segmentation by cellular automaton on GPU

Abstract: The formulation of the FBA in the form of a CA is simple, efficient and straightforward, and can be implemented in low cost vendor-independent graphics hardware. The method can efficiently be applied to perform organ segmentation and quantitative evaluation in clinical routine.

Cited by 47 publications

References 32 publications

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

Automated Brain Tumor Segmentation Using Hsom

High-resolution image segmentation using fully parallel mean shift

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