Automatic segmentation of the brain in MRI

Atkins, M. Stella; Mackiewich, Blair T.

doi:10.1007/bfb0046960

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…The partial volume effect will hinder the detection of individual tissues in most occasions. Various MR image segmentation algorithms, based on the statistical methods (Raya, 1990;Choi et al, 1991;Held et al, 1997;Zavaljevski et al, 2000), the intensity-based methods (Gerig et al, 1992;Suzuki and Toriwaki, 1991), the model-based methods (Aboutanos and Dawant, 1997;Atkins and Mackiewich, 1996;Duta and Sonka, 1998;Leemput et al, 1999a;Santago and Gage, 1993;Yezzi et al, 1997), the fuzzy clustering and the neural network approaches (Alirezaie et al, 1997;Karayiannis and Pai, 1999), have been developed for the tissue detection. However, these approaches have somewhat problems in the detection of MR images.…”

Section: Introductionmentioning

confidence: 99%

Tissue separation in MR images—from supervised to unsupervised classification

Chan

Cheng

Hsieh

2004

Journal of Visual Communication and Image Representation

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Section: Introductionmentioning

confidence: 99%

Tissue separation in MR images—from supervised to unsupervised classification

Chan

Cheng

Hsieh

2004

Journal of Visual Communication and Image Representation

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Model creation and deformation for the automatic segmentation of the brain in MR images

Aboutanos

Nikanne

Watkins

et al. 1999

IEEE Trans. Biomed. Eng.

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In this paper a method for the automatic segmentation of the brain in magnetic resonance images is presented and validated. The proposed method involves two steps 1) the creation of an initial model and 2) the deformation of this model to fit the exact contours of the brain in the images. A new method to create the initial model has been developed and compared to a more traditional approach in which initial models are created by means of brain atlases. A comprehensive validation of the complete segmentation method has been conducted on a series of three-dimensional T1-weighted magnetization-prepared rapid gradient echo image volumes acquired both from control volunteers and patients suffering from Cushing's disease. This validation study compares results obtained with the method we propose and contours drawn manually. Averages differences between manual and automatic segmentation with the model creation method we propose are 1.7% and 2.7% for the control volunteers and the Cushing's patients, respectively. These numbers are 1.8% and 5.6% when the atlas-based method is used.

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Fully automatic segmentation of the brain in MRI

Atkins

Mackiewich²

1998

IEEE Trans. Med. Imaging

334

188

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A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different-resolution images and different echo sequences. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and "snakes" contouring techniques, and a priori knowledge, which is used to remove the eyes, which are tricky to remove based on image intensity alone. It is a multistage process, involving first removal of the background noise leaving a head mask, then finding a rough outline of the brain, then refinement of the rough brain outline to a final mask. The paper describes the main features of the method, and gives results for some brain studies.

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Automatic segmentation of the brain in MRI

Cited by 4 publications

References 7 publications

Tissue separation in MR images—from supervised to unsupervised classification

Tissue separation in MR images—from supervised to unsupervised classification

Model creation and deformation for the automatic segmentation of the brain in MR images

Fully automatic segmentation of the brain in MRI

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