Amir A. Khaliq

A modified POCS‐based reconstruction method for compressively sampled MR imaging

One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled k‐space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft‐thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Marys Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal‐to‐noise ratio, artifact power, and correlation as compared to the reconstruction through low‐resolution and POCS algorithms.

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.

Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.

Temporal Correlation-Based Spatial Filtering of Rician Noise for Functional MRIs

A novel correlation-based filter is presented for de-noising functional magnetic resonance imaging (fMRI) data. Temporal correlation-based exponential weights are defined for spatial smoothing of the data, with bias reduction using estimated noise variance. The proposed scheme is tested on simulated and real fMRI data. Finally, the results are compared with conventional filters. The method is found to be effectively suppressing the Rician noise in fMRI data, while improving the SNR.

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Diabetic retinopathy (DR) is the major ophthalmic disorder because of variation in veins structure which may cause blindness. The retinal vein morphology distinguishes the progressive phases of various sight debilitating maladies and consequently clears an approach to characterize its seriousness. The proposed method for retinal blood vessels detection consists of two major processes: denoising and vasculature segmentation. First, we used denoising preprocessing steps which comprises of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement along with morphological filters to remove low frequency noise, followed by masking to excerpt Region Of Interest (ROI) and difference image of low pass filter to suppress high frequency noise. Adaptive thresholding has been used for vessels segmentation, followed by postprocessing to eliminate unconnected pixels and to obtain Vessel Location Map (VLM). Dilate operation has been used to enhance vessels diameter. In the second step, Combination Of Shifted Filter Responses (B-COSFIRE) for vasculature enhancement along with adaptive thresholding has been applied to segment vessel and background pixels. B represents the bar/vessel like structure. Finally, using pixel by pixel AND operation between VLM and the output of adaptive thresholding, to obtain desired binary image. The proposed framework has been validated on DRIVE and STARE images datasets and compared with other recent approaches for retinal blood vessels segmentation. The proposed scheme provides good results in the term of Accuracy (Acc), Sensitivity (Se) and Specificity (Sp) as compared to other competing methods.

Unmixing functional magnetic resonance imaging data using matrix factorization

Functional magnetic resonance imaging (fMRI) data is processed by different techniques for detection of activated voxels including principal component analysis (PCA), independent component analysis (ICA), non‐negative matrix factorization (NMF), and so on. In this work, a modified version of NMF method is proposed in which data is not supposed to be non‐negative. The proposed scheme is applied to synthetic fMRI data along with NMF conventional method. The results of the proposed scheme show that it is not only computationally efficient but also has good quality results as compared to that of NMF in terms of average correlation. Finally, proposed method is applied to monkeys fMRI data, and the results are compared with that of NMF and ICA.

A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends

The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.

Poisson noise reduction in scintigraphic images using Gradient Adaptive Trimmed Mean filter

We propose a new hybrid technique for reduction of poisson noise in scintigraphic images. Our proposed method is a combination of Gradient calculation and Adaptive Trimmed Mean filter (ATMF). In a predefined window, gradient of the center pixel is averaged out. ATMF remove the lowest and highest variations in the pixel values of Gradient denoised image and average out remaining neighborhood pixel values. The proposed technique is applied on scintigraphic images. Results are compared with conventional filters i.e. Median, Wiener filter and latest denoising filter i.e. Non Local Mean (NLM) filter. The proposed scheme shows good visual results with improving Correlation, Mean Squared Error (MSE), Structural Similarity Index Metric (SSIM) and Peak to Signal Noise Ratio (PSNR) of the image.

Source extraction from functional magnetic resonance imaging data using ICA based on fourth-order and exponential contrast functions

Independent component analysis (ICA) is one of the well-known statistical techniques used for blind source separation. It is also used for the extraction of sources from functional magnetic resonance imaging (fMRI) data. Benchmark for different ICA algorithms is speed and accuracy. In this article, we will be focusing on two simple contrast functions along with matrix-based updating rules. Fixed-point iteration is used for optimization of the contrast functions. Application of matrix-based weight updating makes the process converge rapidly. Validity of the algorithms is tested by comparing the speed and accuracy on simulated and actual fMRI data with other conventional ICA approaches.