An accurate assessment of iron accumulation is required for diagnosis and therapy of iron overload in various neurodegenerative diseases. Susceptibility Weighted Imaging (SWI) offers information about any tissue that has a different susceptibility than its surrounding structures. Reliable methods to precisely quantify brain iron are essential. Image segmentation refers to partition of an image into different regions that differ in some characteristics. Accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important for a correct diagnosis by clinical tools. In this paper, an experimental analysis is done using fuzzy c-means and k-means segmentation algorithm for detection of iron content in SWI brain images.

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Zone plate cameras are used for high sensitivity imaging of X- and gamma-ray sources. The image thus obtained requires decoding and numerical techniques are devised for this purpose. The response function and the signal-to-noise ratio for a single point source have been evaluated for zone plates with varying numbers of zones. Autocorrelation is the best of the methods examined, and the results are in good aggreement with those obtained using Monte Carlo techniques. The autocorrelation method can give a gain of 50% in signal-to-noise for a single point source over the maximum obtainable with optical decoding. Criteria for the choice of number of zones are discussed.

Magnetic Resonance Images (MRI) are usually prone to noise like Rician and Gaussian noise. It is very difficult to perform image processing functions with the presence of noise. The objective of our work is to investigate the best method for denoising the MRI images. This study included 25 MRI subjects selected from the Open Access Series of Imaging Studies (OASIS) - 3 database. The 25 brain image subjects includes cases of both men and women aged 60 to 80. The input RGB image is first converted to gray scale image in which the contrast, sharpness, shadow and structure of the color of image are preserved. The proposed work uses an improved Gaussian smoothing technique for denoising Magnetic Resonance Images by constructing a modified mask for Gaussian smoothing. The performance of the proposed technique has been compared with various filters like median filter, Gaussian filter and Gabor filter. The performance evaluation was carried out by metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) index. The experimental results show that the Improved Gaussian Smoothing Technique (IGST) performs better than other methods. All experiments were conducted using Scikit Learn version 0.20 and Scikit Image version 0.14.1 under Python version 3.6.7.