http://brainmap.org/pubs/Kogler_Neuroimage_15.pdf

Psychosocial versus physiological stress - Meta-analyses on deactivations and activations of the neural correlates of stress reactions

Medical imaging systems often require image enhancement to visualize images of the human body and its organs. This would help medical professionals in irregularity or abnormality detection and diagnosis. This paper demonstrates a method to enhance medical related images. The proposed algorithm uses techniques, such as, guided filtering, edge enhancement, contrast stretching, and image fusion to enhance low resolution images. Visually, the proposed method produces better or comparable enhanced images than several state-of-the-art methods. In addition, we also test the performance of the proposed method with the method mentioned in [1].

A versatile edge preserving image enhancement approach for medical images using guided filter

Diffusion weighted (DW) magnetic resonance (MR) imaging maps the diffusion process of water in the tissues. DW-MR image is useful to probe the tissue microstructure, but suffers from inherent low signal to noise ratio and poor contrast. Dynamic stochastic resonance (DSR) utilizes the noise to enhance the low contrast image where the quality of the processed image depends on the bistability parameters of DSR and the number of iterations. This paper presents an approach that optimally finds the bistability parameters and number of iterations for the maximization of competitive image quality indices: contrast enhancement factor and mean opinion score using multi-objective particle swarm optimization. The proposed Particle Swarm Optimization optimized DSR algorithm has been tested on 40 DW-MR brain images of different subjects. The quantified results show average contrast enhancement factor, 1.603 and average perceptual quality measure, 9.508. These values are significantly higher than image quality indices of original image, the images that are produced by conventional enhancement methods and filtering followed by enhancement methods.

Dynamic Stochastic Resonance Based Diffusion-Weighted Magnetic Resonance Image Enhancement Using Multi-Objective Particle Swarm Optimization

Image analysis plays a crucial role in the field of medicine, as the analysis guides the radiologist towards perfect diagnosis and treatment planning. This paper presents a novel method to enhance computerized tomography (CT) and magnetic resonance (MR) images. The proposed algorithm uses three techniques, namely, Domain Transform, Shape-adaptive edge enhancement, and Adaptive histogram equalization to enhance low resolution images. Visually, the proposed method produces better or comparable enhanced images than several state-of-the-art methods. In addition, we also test the performance of the proposed method with the method mentioned in [1] using EME and EMEE quality measures.

A Novel Technique to Enhance Low Resolution CT and Magnetic Resonance Images in Cloud

This paper presents a novel method to enhance computerized tomography (CT) and magnetic resonance (MR) images. The proposed algorithm uses three techniques, namely, Domain Transform, Shape-adaptive edge enhancement, and Adaptive histogram equalization to enhance low resolution images. Visually, the proposed method produces better or comparable enhanced images than several state-of-the-art methods [1--4]. In addition, we also test the performance of the proposed method with the method mentioned in [1] using EME [5] and EMEE [6] quality measures.

A novel technique to enhance low resolution CT and magnetic resonance images

In this paper, a non-linear operator is presented to increase the contrast and enhance magnetic resonance (MR) cardiac images The operator is based on the iterative image enhancement method introduced by Kramer and Bruckner (1984) Angiographic images pose a difficult problem to image analysis techniques because of the non-homogeneity of the heart edges (as normally occurs in MR images) The multi-scale operator presented in the paper improves the image for this purpose by increasing the strength of the edges

http://ieeexplore.ieee.org/iel4/5216/14214/00651959.pdf

Enhancement of MR images using non-linear techniques

SELFIE FOR WORK-BASED LEARNING: A TOOL TO SUPPORT VET INSTITUTIONS AND COMPANIES BECOME FIT FOR THE DIGITAL AGE 

Selfie for work-based learning: a tool to support vet institutions and companies become fit for the digital age

The authors have developed an authoring language for specification of algorithms, where steps of standardized management procedures can be concisely described, stored in a computer format, and disseminated over a network or the World Wide Web. Those steps can be shown in a flowchart representation, where each node can be linked to multimedia resources. The authors' method can be used to display clinical practice guidelines, protocols, alerts or actions to follow in any clinical problem, instrumentation or established procedure. Such approach can be applied to assess clinical impact of different technologies and health policies, giving feedback to their developers.

A specification language for clinical algorithms

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Enhancement of MR images using non-linear techniques

Selfie for work-based learning: a tool to support vet institutions and companies become fit for the digital age

A specification language for clinical algorithms