Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Study of Target Enhancement Algorithms to Counter the Hostile Nuclear Environment

A necessary requirement of strategic defense is the detection of incoming nuclear warheads in an environment that may include nuclear detonations of undetected or missed target warheads. A computer model is described which simulates incoming warheads as distant endoatmospheric targets. A model of the expected electromagnetic noise present in a nuclear environment is developed using estimates of the probability distributions. Predicted atmospheric effects are also included. Various image enhancement algorithms, both linear and nonlinear, are discussed concerning their anticipated ability to suppress the noise and atmospheric effects of the nuclear environment. These algorithms are then tested, using the combined target and noise models, and evaluated in terms of the stated figures of merit

Restauration d'images en IRM anatomique pour l'étude préclinique des marqueurs du vieillissement cérébral

Les maladies neurovasculaires et neurodégénératives liées à l'âge sont en forte augmentation. Alors que ces changements pathologiques montrent des effets sur le cerveau avant l'apparition de symptômes cliniques, une meilleure compréhension du processus de vieillissement normal du cerveau aidera à distinguer l'impact des pathologies connues sur la structure régionale du cerveau. En outre, la connaissance des schémas de rétrécissement du cerveau dans le vieillissement normal pourrait conduire à une meilleure compréhension de ses causes et peut-être à des interventions réduisant la perte de fonctions cérébrales associée à l'atrophie cérébrale. Par conséquent, ce projet de thèse vise à détecter les biomarqueurs du vieillissement normal et pathologique du cerveau dans un modèle de primate non humain, le singe marmouset (Callithrix Jacchus), qui possède des caractéristiques anatomiques plus proches de celles des humains que de celles des rongeurs. Cependant, les changements structurels (par exemple, de volumes, d'épaisseur corticale) qui peuvent se produire au cours de leur vie adulte peuvent être minimes à l'échelle de l'observation. Dans ce contexte, il est essentiel de disposer de techniques d'observation offrant un contraste et une résolution spatiale suffisamment élevés et permettant des évaluations détaillées des changements morphométriques du cerveau associé au vieillissement. Cependant, l'imagerie de petits cerveaux dans une plateforme IRM 3T dédiée à l'homme est une tâche difficile car la résolution spatiale et le contraste obtenus sont insuffisants par rapport à la taille des structures anatomiques observées et à l'échelle des modifications attendues. Cette thèse vise à développer des méthodes de restauration d'image pour les images IRM précliniques qui amélioreront la robustesse des algorithmes de segmentation. L'amélioration de la résolution spatiale des images à un rapport signal/bruit constant limitera les effets de volume partiel dans les voxels situés à la frontière entre deux structures et permettra une meilleure segmentation tout en augmentant la reproductibilité des résultats. Cette étape d'imagerie computationnelle est cruciale pour une analyse morphométrique longitudinale fiable basée sur les voxels et l'identification de marqueurs anatomiques du vieillissement cérébral en suivant les changements de volume dans la matière grise, la matière blanche et le liquide cérébral.Age-related neurovascular and neurodegenerative diseases are increasing significantly. While such pathological changes show effects on the brain before clinical symptoms appear, a better understanding of the normal aging brain process will help distinguish known pathologies' impact on regional brain structure. Furthermore, knowledge of the patterns of brain shrinkage in normal aging could lead to a better understanding of its causes and perhaps to interventions reducing the loss of brain functions. Therefore, this thesis project aims to detect normal and pathological brain aging biomarkers in a non-human primate model, the marmoset monkey (Callithrix Jacchus) which possesses anatomical characteristics more similar to humans than rodents. However, structural changes (e.g., volumes, cortical thickness) that may occur during their adult life may be minimal with respect to the scale of observation. In this context, it is essential to have observation techniques that offer sufficiently high contrast and spatial resolution and allow detailed assessments of the morphometric brain changes associated with aging. However, imaging small brains in a 3T MRI platform dedicated to humans is a challenging task because the spatial resolution and the contrast obtained are insufficient compared to the size of the anatomical structures observed and the scale of the xpected changes with age. This thesis aims to develop image restoration methods for preclinical MR images that will improve the robustness of the segmentation algorithms. Improving the resolution of the images at a constant signal-to-noise ratio will limit the effects of partial volume in voxels located at the border between two structures and allow a better segmentation while increasing the results' reproducibility. This computational imaging step is crucial for a reliable longitudinal voxel-based morphometric analysis and for the identification of anatomical markers of brain aging by following the volume changes in gray matter, white matter and cerebrospinal fluid

Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex Wavelet Domain

Many clinicians regard speckle noise as an undesirable artifact in ultrasound images masking the underlying pathology within a patient. Speckle noise is a random interference pattern formed by coherent radiation in a medium containing many sub-resolution scatterers. Speckle has a negative impact on ultrasound images as the texture does not reflect the local echogenicity of the underlying scatterers. Studies have shown that the presence of speckle noise can reduce a physician's ability to detect lesions by a factor of eight. Without speckle, small high-contrast targets, low contrast objects, and image texture can be deduced quite readily. Speckle filtering of medical ultrasound images represents a critical pre-processing step, providing clinicians with enhanced diagnostic ability. Efficient speckle noise removal algorithms may also find applications in real time surgical guidance assemblies. However, it is vital that regions of interests are not compromised during speckle removal. This research pertains to the reduction of speckle noise in ultrasound images while attempting to retain clinical regions of interest. Recently, the advance of wavelet theory has lead to many applications in noise reduction and compression. Upon investigation of these two divergent fields, it was found that the speckle noise tends to rotate an image's homomorphic complex-wavelet coefficients. This work proposes a new speckle reduction filter involving a counter-rotation of these complex-wavelet coefficients to mitigate the presence of speckle noise. Simulations suggest the proposed denoising technique offers superior visual quality, though its signal-to-mean-square-error ratio (S/MSE) is numerically comparable to adaptive frost and kuan filtering. This research improves the quality of ultrasound medical images, leading to improved diagnosis for one of the most popular and cost effective imaging modalities used in clinical medicine

Time-frequency analysis based on split spectrum applied to audio and ultrasonic signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSignal processing is a large subject with applications integral to a number of technological fields such as communication, audio, Voice over IP (VoIP), pattern recognition, sonar, radar, ultrasound and medical imaging. Techniques exist for the analysis, modelling, extraction, recognition and synthesis of signals of interest. The focus of this thesis is signal processing for acoustics (both sonic and ultrasonic). In the applications examined, signals of interest are usually incomplete, distorted and/or noisy. Therefore, reconstructing the signal, noise reduction and removal of any distortion/interference are the main goals of the signal processing techniques presented. The primary aim is to study and develop an advanced time-frequency signal processing technique for acoustic applications to enhance the quality of the signals. In the first part of the thesis, a technique is presented that models and maintains the correlation between temporal and spectral parameters of audio signals. A novel Packet Loss Concealment (PLC) method is developed with applications to VoIP, audio broadcasting, and streaming. The problem of modelling the time-varying frequency spectrum in the context of PLC is addressed, and a novel solution is proposed for tracking and using the temporal motion of spectral flow to reconstruct the signal. The proposed method utilises a Time-Frequency Motion (TFM) matrix representation of the audio signal, where each frequency is tagged with a motion vector estimate that is assessed by cross-correlation of the movement of spectral energy within sub-bands across time frames. The missing packets are estimated using extrapolation or interpolation algorithms using a TFM matrix and then inverse transformed to the time-domain for reconstruction of the signal. The proposed method is compared with conventional approaches using objective Performance Evaluation of Speech Quality (PESQ), and subjective Mean Opinion Scores (MOS) in a range of packet loss from 5% to 20%. The evaluation results demonstrate that the proposed algorithm substantially improves performance by an average of 2.85% and 5.9% in terms of PESQ and MOS respectively. In the second part of the thesis, the proposed method is extended and modified to address challenges of excessive coherent noise arising from ultrasonic signals gathered during Guided Wave Testing (GWT). It is an advanced Non-destructive testing technique which is used over several branches of industry to inspect large structures for defects where the structural integrity is of concern. In such systems, signal interpretation can often be challenging due to the multi-modal and dispersive propagation of Ultrasonic Guided Waves (UGWs). The multi-modal and dispersive nature of the received signals hampers the ability to detect defects in a given structure. The Split-Spectrum Processing (SSP) method with application for such signal has been studied and reviewed quantitatively to measure the enhancement in terms of Signal-to-Noise Ratio (SNR) and spatial resolution. In this thesis, the influence of SSP filter bank parameters on these signals is studied and optimised to improve SNR and spatial resolution considerably. The proposed method is compared analytically and experimentally with conventional approaches. The proposed SSP algorithm substantially improves SNR by an average of 30dB. The conclusions reached in this thesis will contribute to the progression of the GWT technique through considerable improvement in defect detection capability.Centre for Electronic Systems Research (CESR) of Brunel University London, The National Structural Integrity Research Centre (NSIRC) and TWI Ltd

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based