Literature DB >> 8938037

Tumour volume determination from MR images by morphological segmentation.

P Gibbs1, D L Buckley, S J Blackband, A Horsman.   

Abstract

Accurate tumour volume measurement from MR images requires some form of objective image segmentation, and therefore a certain degree of automation. Manual methods of separating data according to the various tissue types which they are thought to represent are inherently prone to operator subjectivity and can be very time consuming. A segmentation procedure based on morphological edge detection and region growing has been implemented and tested on a phantom of known adjustable volume. Comparisons have been made with a traditional data thresholding procedure for the determination of tumour volumes on a set of patients with intracerebral glioma. The two methods are shown to give similar results, with the morphological segmentation procedure having the advantages of being automated and faster.

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Year:  1996        PMID: 8938037     DOI: 10.1088/0031-9155/41/11/014

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  20 in total

1.  Computerised volumetric analysis of lesions in multiple sclerosis using new semi-automatic segmentation software.

Authors:  P Dastidar; T Heinonen; T Vahvelainen; I Elovaara; H Eskola
Journal:  Med Biol Eng Comput       Date:  1999-01       Impact factor: 2.602

2.  3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

Authors:  Karteek Popuri; Dana Cobzas; Albert Murtha; Martin Jägersand
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-08-11       Impact factor: 2.924

3.  Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI.

Authors:  Junghoon Lee; Jonghye Woo; Fangxu Xing; Emi Z Murano; Maureen Stone; Jerry L Prince
Journal:  Comput Med Imaging Graph       Date:  2014-08-01       Impact factor: 4.790

4.  A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data.

Authors:  Jan Egger; Christoph Kappus; Bernd Freisleben; Christopher Nimsky
Journal:  J Med Syst       Date:  2011-03-08       Impact factor: 4.460

5.  Volumetric measurement of human calf muscle from magnetic resonance imaging.

Authors:  M A Elliott; G A Walter; H Gulish; A S Sadi; D D Lawson; W Jaffe; E K Insko; J S Leigh; K Vandenborne
Journal:  MAGMA       Date:  1997-06       Impact factor: 2.310

6.  Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Authors:  Pradipta Maji; Shaswati Roy
Journal:  PLoS One       Date:  2015-04-07       Impact factor: 3.240

7.  Lesion segmentation and manual warping to a reference brain: intra- and interobserver reliability.

Authors:  J A Fiez; H Damasio; T J Grabowski
Journal:  Hum Brain Mapp       Date:  2000-04       Impact factor: 5.038

8.  Segmentation of malignant gliomas through remote collaboration and statistical fusion.

Authors:  Zhoubing Xu; Andrew J Asman; Eesha Singh; Lola Chambless; Reid Thompson; Bennett A Landman
Journal:  Med Phys       Date:  2012-10       Impact factor: 4.071

9.  Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics.

Authors:  Shonket Ray; Rosalie Hagge; Marijo Gillen; Miguel Cerejo; Shidrokh Shakeri; Laurel Beckett; Tamara Greasby; Ramsey D Badawi
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

10.  Automatic brain tumor segmentation by subject specific modification of atlas priors.

Authors:  Marcel Prastawa; Elizabeth Bullitt; Nathan Moon; Koen Van Leemput; Guido Gerig
Journal:  Acad Radiol       Date:  2003-12       Impact factor: 3.173

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