Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic

Contrast enhancement by combining T1- and T2-weighted structural brain MR Images

PURPOSE

In order to more precisely differentiate cerebral structures in neuroimaging studies, a novel technique for enhancing the tissue contrast based on a combination of T1-weighted (T1w) and T2-weighted (T2w) MRI images was developed.

METHODS

The combined image (CI) was calculated as CI = (T1w - sT2w)/(T1w + sT2w), where sT2w is the scaled T2-weighted image. The scaling factor was calculated to adjust the gray- matter (GM) voxel intensities in the T2w image so that their median value equaled that of the GM voxel intensities in the T1w image. The image intensity homogeneity within a tissue and the discriminability between tissues in the CI versus the separate T1w and T2w images were evaluated using the segmentation by the FMRIB Software Library (FSL) and FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Boston, MA) software.

RESULTS

The combined image significantly improved homogeneity in the white matter (WM) and GM compared to the T1w images alone. The discriminability between WM and GM also improved significantly by applying the CI approach. Significant enhancements to the homogeneity and discriminability also were achieved in most subcortical nuclei tested, with the exception of the amygdala and the thalamus.

CONCLUSION

The tissue discriminability enhancement offered by the CI potentially enables more accurate neuromorphometric analyses of brain structures.

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Background:

Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.

Methods:

This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.

Results:

The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.

Conclusions:

In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.