Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Neurotransmitter Functional Role in Neurodegenerative Diseases: human Health

Neuroscience is the study of'tbe ne rvous system , including the i - ; . in, spinal cord and peripheral nerves . Neurons are the basic cells of the brain and nervous system which exerts its functional role through various neurotransmitters and receptor systems . The activity of a nen ren depends on the balance between the number of excitatory and inhibito r y processes affecting it, both processes occurring individually and sin ,tlte-' ,ieously. The functional bal,ince of different neurotransmitters such as Acct >>lcholine (Ach), Dopamine (DA), Serotonin (5-1-17), Nor epinepbri,te (N.1 j, Epinephrine (LPI), Glutamate and Gamma amino butyric acid (GA BA) regulates the growth , division and other vital functions ofa normal cell / organisin (Sudha, 1 998). The micro-environ ; nertt of the cell is controlled / the macro-environment that surrounds the individual. Any change in the cell environment causes imbalance in cell homeostasis and f,ntction. Pollution is a significant cause of imbalance caused iii the inacYcenvironment. Interaction with polluted environments can have an adverse impact on the health of humans. The alarming rise in enviromilmieil cont.iniin :rtion has been linked to rises in levels of pesticides, ndltstr al effluents, domestic Waste, car exhausts and other anthropogenic activities. Persistent exposures to contaminant cause a negative imp,-, on brain health and development . Pollution also causes a change in the neurotransmitters and their receptor function leading to earl.;' recurrence of neurodcge,terative disorders such as flypoxia , Alzbeimers's and Huntington 's disease early in life.

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images