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Accurate automatic classification of major tissue classes and thecerebrospinal fluid in pediatric MR scans of early childhood brains remains achallenge. A poor and highly variable grey matter and white matter contrast onT1-weighted MR scans of developing brains complicates the automaticcategorization of voxels into major tissue classes using state-of-the-artclassification methods (Partial Volume Estimation). Varying intensities acrossbrain tissues and possible tissue artifacts further contribute tomisclassification. In order to improve the accuracy of automatic detection ofmajor tissue types and the cerebrospinal fluid in infant's brains within theage range from 10 days to 4.5 years, we propose a new classification methodbased on Kernel Fisher Discriminant Analysis (KFDA) for pattern recognition,combined with an objective structural similarity index (SSIM) for perceptualimage quality assessment. The proposed method performs an optimal partitioningof the image domain into subdomains having different average intensity valuesand relatively homogeneous tissue intensity. In the KFDA-based framework, acomplex non-linear structure of grey matter, white matter and cerebrospinalfluid intensity clusters in a 3D (T1W, T2W, PDW)-space is exploited to find anaccurate classification. Based on Computer Vision hypothesis that the HumanVisual System is an optimal structural information extractor, the SSIM finds anew role in the evaluation of the quality of classification. A comparison withthe state-of-the-art Partial Volume Estimation method using the SSIM indexdemonstrates superior performance of the local KFDA-based algorithm in lowcontrast subdomains and more accurate detection of grey matter, white matter,and cerebrospinal fluid patterns in the brain volume.

Novel solutions toward high accuracy automatic brain tissue classification in young children