Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior

Chang, Junbiao; Chou, Tom

doi:10.1007/s10851-013-0440-9

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…We segmented the moving wavefront of the CSD waves using a variant of the regularized front segmentation method presented in Chang et al 2012, 51 modified by the shape prior renormalization scheme of Chang et al 2014. 52 In order to improve the signal to noise ratio, we used bilinear interpolation to downsize the original 644 Â 480 movies to 322 Â 240. In the frame-by-frame differences of these movies, the region immediately upwind of the CSD wavefront is brighter than the surrounding regions.…”

Section: Wavefront Tracings and Wavespeed Mapsmentioning

confidence: 99%

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Heterogeneous incidence and propagation of spreading depolarizations

Kaufmann

Theriot

Zyuzin

et al. 2016

J Cereb Blood Flow Metab

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Spreading depolarizations are implicated in a diverse set of neurologic diseases. They are unusual forms of nervous system activity in that they propagate very slowly and approximately concentrically, apparently not respecting the anatomic, synaptic, functional, or vascular architecture of the brain. However, there is evidence that spreading depolarizations are not truly concentric, isotropic, or homogeneous, either in space or in time. Here we present evidence from KCl-induced spreading depolarizations, in mouse and rat, in vivo and in vitro, showing the great variability that these depolarizations can exhibit. This variability can help inform the mechanistic understanding of spreading depolarizations, and it has implications for their phenomenology in neurologic disease.

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Section: Wavefront Tracings and Wavespeed Mapsmentioning

confidence: 99%

“…(g) Varying propagation rate, possibly modulated by midline vein. Map at right plots derived SD velocity over each pixel, highlighting heterogeneity (methods in Chang et al 51,52 ).…”

Section: Possible Cytoarchitectonic Modulationmentioning

confidence: 99%

Heterogeneous incidence and propagation of spreading depolarizations

Kaufmann

Theriot

Zyuzin

et al. 2016

J Cereb Blood Flow Metab

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“…In recent years, many scholars have attempted to fuse graph cut and shape prior model. The fused results include parameter-adaptive shape prior constraint algorithm based on kernel principal component analysis (KPCA), adaptive shape prior model [8], and graph cut and nonlinear statistical shape prior algorithm [9]. For the effectiveness of image segmentation, some scholars included image features like color, grayscale, and texture into the algorithm design [10].…”

Section: Introductionmentioning

confidence: 99%

Fusion Between Shape Prior and Graph Cut for Vehicle Image Segmentation

Wu¹,

Sun²,

Liu³

et al. 2020

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In vehicle image segmentation, the traditional graph cut algorithm often have errors, when the original image contains shadows or complex background. To overcome the errors, this paper introduces the shape prior to graph cut algorithm. Our algorithm firstly maps the vehicle image to a weighted undirected graph, and obtains the regional energy and boundary energy functions. Then, the shape prior was added to constrain the image segmentation, and create a new energy function. The star convex was selected as the shape prior, and manually marked. Experimental results show that our algorithm segmented complex background vehicle images, shadowed vehicle images, and incomplete vehicle images more effectively than Otsu's method and graph cut algorithm. The details of vehicles were preserved and extracted efficiently by our algorithm. The research results provide new insights into vehicle image segmentation.

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