An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image's pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

Comparison of Randomized Multifocal Mapping and Temporal Phase Mapping of Visual Cortex for Clinical Use

fMRI is becoming an important clinical tool for planning and guidance of surgery to treat brain tumors, arteriovenous malformations, and epileptic foci. For visual cortex mapping, the most popular paradigm by far is temporal phase mapping, although random multifocal stimulation paradigms have drawn increased attention due to their ability to identify complex response fields and their random properties. In this study we directly compared temporal phase and multifocal vision mapping paradigms with respect to clinically relevant factors including: time efficiency, mapping completeness, and the effects of noise. Randomized, multifocal mapping accurately decomposed the response of single voxels to multiple stimulus locations and made correct retinotopic assignments as noise levels increased despite decreasing sensitivity. Also, multifocal mapping became less efficient as the number of stimulus segments (locations) increased from 13 to 25 to 49 and when duty cycle was increased from 25% to 50%. Phase mapping, on the other hand, activated more extrastriate visual areas, was more time efficient in achieving statistically significant responses, and had better sensitivity as noise increased, though with an increase in systematic retinotopic mis-assignments. Overall, temporal phase mapping is likely to be a better choice for routine clinical applications though random multifocal mapping may offer some unique advantages for selected applications

Algorithmic Analysis Techniques for Molecular Imaging

This study addresses image processing techniques for two medical imaging modalities: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), which can be used in studies of human body functions and anatomy in a non-invasive manner. In PET, the so-called Partial Volume Effect (PVE) is caused by low spatial resolution of the modality. The efficiency of a set of PVE-correction methods is evaluated in the present study. These methods use information about tissue borders which have been acquired with the MRI technique. As another technique, a novel method is proposed for MRI brain image segmen- tation. A standard way of brain MRI is to use spatial prior information in image segmentation. While this works for adults and healthy neonates, the large variations in premature infants preclude its direct application. The proposed technique can be applied to both healthy and non-healthy premature infant brain MR images. Diffusion Weighted Imaging (DWI) is a MRI-based technique that can be used to create images for measuring physiological properties of cells on the structural level. We optimise the scanning parameters of DWI so that the required acquisition time can be reduced while still maintaining good image quality. In the present work, PVE correction methods, and physiological DWI models are evaluated in terms of repeatabilityof the results. This gives in- formation on the reliability of the measures given by the methods. The evaluations are done using physical phantom objects, correlation measure- ments against expert segmentations, computer simulations with realistic noise modelling, and with repeated measurements conducted on real pa- tients. In PET, the applicability and selection of a suitable partial volume correction method was found to depend on the target application. For MRI, the data-driven segmentation offers an alternative when using spatial prior is not feasible. For DWI, the distribution of b-values turns out to be a central factor affecting the time-quality ratio of the DWI acquisition. An optimal b-value distribution was determined. This helps to shorten the imaging time without hampering the diagnostic accuracy.Siirretty Doriast

New neuroimaging methods for clinical neuroscience and neurological disorders

236 p.Clinical neuroscience today makes use of state-of-the-art neuroimaging to study structural and functional brain data to improve diagnosis and prognosis in different neurological disorders.In this thesis dissertation, I focused on Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging modality to study brain functional and structural data. Different new methods for brain connectivity analysis are described and applied to three pathologies: Disorder of Consciousness, Alzheimer's Disease and Traumatic Axonal Injury. This work is at the frontiers between two fields, the Biomedical Engineering of Image Processing and the Clinical Neuroscience

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states