4 research outputs found

    Flow chart of the hybrid classifier, coupling ICA, SVM and IFLDA for brain MRI classification and segmentation.

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    <p>First, the pre-processing step included registering FLAIR and T2WI with T1WI and correcting intensity inhomogeneity correction using N3 method. Second, the entire volume data of multislice-multispectral MR image data are automatically sphered to be a new data set by using ICA to remove the first two order statistics. Third, a small set of training data, containing a 3x3 matrix (of 9 pixels) of GM, WM, CSF, and background (BG) was manually identified by operators from a specific image slice of 3D images for SVM classification of the sphered multispectral images. At the same time, all the sphered multispectral images go through skull striping with BET. Finally, the output of SVM serves as a large pool of training samples for initiation of an iterative version of FLDA,</p

    The results of brain classification images from 3D multispectral-multislice MRI.

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    <p>Left side reveals 3D multispectral MRI of FLAIR, T1WI and T2WI and right side is the classification images. Upper, middle and lower rows show GM, WM and CSF images. (A) A 20 year old young female with 587.2 ml, 433.6 ml and 154.8 ml of GM, WM and CSF, and 49.9%, 36.9% and 13.2% of GM, WM and CSF volume fractions. (B) A 60 year old healthy male with 636.0 ml, 587.3 ml and 326.8 ml of GM, WM and CSF, and 41.0%, 37.9% and 21.1% of GM, WM and CSF volume fractions. (C) A 76 year old dementia patient with 562.3 ml, 454.3 ml and 333.1 ml of GM, WM and CSF, and 41.7%, 33.7% and 24.7% of GM, WM and CSF volume fractions.</p
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