“Right to Privacy and Freedom of Press - Conflicts and Challenges”

India is a signatory to the United Nations Declaration of Human Rights 1948 and the International Covenant on Civil and Political 1966, the two major International instruments, building the foundations of the major democracies and the constitutions of the world. Both these instruments give an independent and upper position to right to privacy compared to right to freedom of speech and expression. The freedom of press finds its place under this right to freedom of speech and expression. Both these rights are the two opposite faces of the same coin. Therefore, without the right of privacy finding an equal place in Indian law compared to right to freedom of speech and expression, the working of democracy would be severely handicapped and violations against citizens rights will be on the rise It was this problem in law and need to bring a balance between these two conflicting rights that induced me to undertake this venture. This heavy burden to bring in a mechanism to balance these two rights culminated in me to undertake this thesis titled “Right to Privacy and Freedom of Press – Conflicts and Challenges

The Press council

This study analyses the role of the Press Council as a champion and guard of free speech. It discusses the extent to which the Council succeeded in achieving its statutory objective of preserving the freedom of the press and maintaining and improving the standards of newspapers and news agencies. It also examines the inherent and in-built weaknesses of the Council and suggests ways and means for restructuring and enlarging its functions.

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

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions