Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image sparsity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared to full k-space acquisition, which results in reduced MRI data acquisition time. The images resulting from MRA are sparse in native representation, hence yielding themselves well to CS. Recently our group has proposed a novel CS method called Region of Interest Compressed Sensing (ROICS) as a part of Region of Interest (ROI) weighted CS. This work aims at the implementation of ROICS for the first time on MRA data to reduce MR data acquisition time. It has been demonstrated qualitatively and quantitatively that ROICS outperforms CS at higher acceleration factors. ROICS technique has been applied to 3D angiograms of the brain data acquired at 1.5T. It helps to reduce the MRA data acquisition time and improves the visualization of arteries. ROICS technique has been applied on 4 brain angiogram data sets at different acceleration factors from 2× to 10×. Reconstructed images show ROICS technique performs better than conventional CS technique and is quantified by the comparative Signal to Noise Ratio (SNR) in the ROI.
Mammography is an effective method for breast cancer detection and breast tumor analysis. In mammography, low dose x-ray is used for imaging, due to which the images are poor in contrast and are contaminated by noise. Hence it is difficult for the radiologist to screen the mammograms for diagnostic signs such as micro calcifications and masses. This ensures the need for image enhancement to aid radiologist. In this paper we present a different algorithm for enhancement of digital mammographic images. The proposed methodology uses mathematical morphology for contrast enhancement and wavelet for denoising. The main contribution of this paper is in differentiating the edge pixels from noise. A quantitative measure of Contrast Improvement Index (CII) and Edge Preservation Index (EPI) are used to evaluate the performance of the algorithm. The algorithm has been tested on a large number of images from standard dataset, comparing the results with the state-of-the-art. By both the analytical indices and ROC analysis, the proposed algorithm shows promising results in early detection of breast cancer and diagnosis.
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