Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme.

Abstract:

:The application of a raw data-based, operator-independent MR segmentation technique to differentiate boundaries of tumor from edema or hemorrhage is demonstrated. A case of a glioblastoma multiforme with gross and histopathologic correlation is presented. The MR image data set was segmented into tissue classes based on three different MR weighted image parameters (T1-, proton density-, and T2-weighted) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition. A radiological examination of the MR images and correlation with fuzzy clustering segmentations was performed. Results were confirmed by gross and histopathology which, to the best of our knowledge, reports the first application of this demanding approach. Based on the results of neuropathologic correlation, the application of FCM MR image segmentation to several MR images of a glioblastoma multiforme represents a viable technique for displaying diagnostically relevant tissue contrast information used in 3D volume reconstruction. With this technique, it is possible to generate segmentation images that display clinically important neuroanatomic and neuropathologic tissue contrast information from raw MR image data.

journal_name

Magn Reson Imaging

authors

Phillips WE 2nd,Velthuizen RP,Phuphanich S,Hall LO,Clarke LP,Silbiger ML

doi

10.1016/0730-725x(94)00093-i

subject

Has Abstract

pub_date

1995-01-01 00:00:00

pages

277-90

issue

2

eissn

0730-725X

issn

1873-5894

pii

0730-725X(94)00093-I

journal_volume

13

pub_type

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