Image segmentation using Scale-Space Random Walks

Rzeszutek, Richard; El-Maraghi, Thomas; Androutsos, Dimitrios

doi:10.1109/icdsp.2009.5201062

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…This is as much of an issue for depth map generation as it is for image segmentation. To alleviate this, we utilize an augmented version of Random Walks known as Scale-Space Random Walks (SSRW) [9]. Using the SSRW, we can preserve global image structure, while still mitigating the effect of noise on the resulting depth maps.…”

Section: Generating Depth Maps Via Random Walksmentioning

confidence: 99%

Semi-automatic 2D to 3D image conversion using a hybrid Random Walks and graph cuts based approach

Phan

Rzeszutek

Androutsos

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we present a semi-automated method for converting conventional 2D images to stereoscopic 3D. User-defined strokes that correspond to a rough estimate of the depth values in the scene are defined for the image of interest. With these strokes, our system thus determines what the depth values are for the rest of the image, producing a depth map that is ultimately used to create a stereoscopic image pair. Our work is based on a similar scheme which employs Random Walks. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic image pair. Combined with the evident shortcomings of the related work, but noting the merits of Random Walks, we propose a system that is a hybrid between Random Walks, and the popular Graph Cuts segmentation paradigm. Both segmentation algorithms are used to generate a final cohesive depth map, thus combining the merits of both frameworks together. The generated results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related Random Walks scheme.

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Section: Generating Depth Maps Via Random Walksmentioning

confidence: 99%

Semi-automatic 2D to 3D image conversion using a hybrid Random Walks and graph cuts based approach

Phan

Rzeszutek

Androutsos

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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show abstract

“…A consequence to this is that Random Walks can be susceptible to noise. To alleviate this, we use an augmented version of Random Walks known as Scale-Space Random Walks (SSRW) [10]. Using the SSRW, we can preserve global image structure, while still mitigating the effect of noise on the resulting depth maps.…”

Section: Generating Depth Maps Via Random Walksmentioning

confidence: 99%

Semi-automatic 2D to 3D image conversion using scale-space Random Walks and a graph cuts based depth prior

Phan

Rzeszutek

Androutsos

2011

2011 18th IEEE International Conference on Image Processing

Self Cite

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In this paper, we present a semi-automated method for converting conventional 2D images into stereoscopic 3D. User-defined strokes corresponding to a rough estimate of the depth values in the scene are defined for the image of interest. With these, our system determines the depth values for the rest of the image, producing a depth map that can be used to create stereoscopic 3D image pairs. Our work is based on a similar scheme, using the Random Walks segmentation paradigm. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic image pair. Combined with its evident shortcomings, but noting the merits, we propose a system employing Random Walks, while incorporating information from the popular Graph Cuts segmentation paradigm. Thus, a final cohesive depth map is produced, combining the merits of both. The results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related work.

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“…So, RW is an efficient and accurate method in low contrast images characterized by noise and weak edges such as PET images (Boellaard et al, 2004;Zaidi and El Naqa, 2010): the behavior of the RW algorithm in the presence of these issues distinguishes it from other approaches (Grady, 2006). Even if the study proposed by Rzeszutek et al (2009) affirms that a drawback to RW algorithm is that it has difficulty producing clean segmentations in the presence of noise, in previous PET studies (Bagci et al, 2011;Stefano et al, 2013;Onoma et al, 2014) the RW method showed accurate results in the delineation of metabolic targets. For these reasons, we propose a fully automatic and operator independent method for the BTV delineation of brain metastases for Gamma Knife treatments based on an extension of the RW algorithm.…”

Section: Introductionmentioning

confidence: 99%

A fully automatic method for biological target volume segmentation of brain metastases

Stefano

Vitabile

Russo

et al. 2016

Int J Imaging Syst Tech

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Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment

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Image segmentation using Scale-Space Random Walks

Cited by 12 publications

References 8 publications

Semi-automatic 2D to 3D image conversion using a hybrid Random Walks and graph cuts based approach

Semi-automatic 2D to 3D image conversion using a hybrid Random Walks and graph cuts based approach

Semi-automatic 2D to 3D image conversion using scale-space Random Walks and a graph cuts based depth prior

A fully automatic method for biological target volume segmentation of brain metastases

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