Topology Simplification Algorithm for the Segmentation of Medical Scans.pdf

Topology Simplification Algorithm for the Segmentation of Medical Scans PDF

Sylvain Jaume

Magnetic Resonance Imaging, Computed Tomography, and other image modalities are routinely used to visualize a particular structure in the patients body. The classification of the image region corresponding to this structure is called segmentation.

A voyage on medical image segmentation algorithms. Kumar SN 1, Lenin Fred A2*, Muthukumar S3*, Region based segmentation algorithm comprises of mainly the region growing, region splitting and region merging techniques. In region growing algorithm, based on the similarity criteria of the seed point with the neighboring pixels, the growing of region will be done and the similarity criteria

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Topology Simplification Algorithm for the Segmentation of Medical Scans.pdf

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Sofya Voigtuh

Topology simplification algorithm for the segmentation of ... Consequently we have developed an algorithm for automatically correcting holes in segmented medical scans while preserving the accuracy of the segmentation. Upon concepts of Discrete Topology, we remove the holes based on the smallest modification to the image. First we detect each hole with a front propagation and a Reeb graph. Then we search for a number of loops around the hole on the

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Mattio Müllers

An Out-of-core Algorithm for Isosurface Topology Simplification Zoe Wood¨ Caltech Hugues Hoppe Microsoft Research Mathieu Desbrun U. of So. Cal. Peter Schroder¨ Caltech Many high-resolution surfaces are created through isosurface extraction from volumetric repre-sentations, obtained by 3D photography, CT, or MRI. Noise inherent in the acquisition process can lead to geometrical and

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Noels Schulzen

09/06/2015 · Hence an automated tumor segmentation algorithm would be advantageous. The scans were carried out on a Biograph TrueV 64 slice PET-CT scanner (Siemens Medical Solutions, Hoffman Estates, IL, USA). PET data were reconstructed into 168 × 168 matrices with pixel size of 4.07 × 4.07 mm. The CT data were reconstructed using a matrix of 512 × 512 pixels with pixel size of 0.98 × 0.98 mm. …

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Jason Leghmann

ally carried out by radiologists or by other trained medical professionals, and is generally time-consuming. Many methods have been proposed to address the aforementioned challenges. Xu et al (2014) introduced an algorithm based on multi-scale Hessian analysis to segment the rib cage from CT scans. In this algorithm a 95% accuracy of rib cage extraction was achieved; however, individual ribs A voyage on medical image segmentation algorithms

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Jessica Kolhmann

the segmentation is to accurately identify the areas that are brain and areas that are CSF. This thesis will: Apply the level set method for an accurate, automated and computationally e cient segmentation of medical images. In this application, CT scans of hydrocephalus brain are used and areas of brain and CSF are segmented in both 2D and 3D.