Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images

The need for transmission and archive of mammograms and ultrasound Images has dramatically increased in tele-healthcare applications. Such images require large amount of' storage space which affect transmission speed. Therefore an effective compression scheme is essential. Compression of these images. in general. laces a great challenge to compromise between the higher compression ratio and the relevant diagnostic information. Out of the many studied compression schemes. lossless . IPl. (i- LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are chosen based on a comprehensive experimental study carried on a large number of mammograms and ultrasound images of different sizes and texture. The lossless schemes are evaluated based on the compression ratio and compression speed. The distortion in the image quality which is introduced by lossy methods evaluated based on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio (PSNR). It is found that lossless compression can achieve a modest compression ratio 2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than lossless ones but at the price of the image quality which may impede diagnostic conclusions. In this work, a new compression approach called Ilvbrid Region-based Image Compression Scheme (IIYRICS) has been proposed for the mammograms and ultrasound images to achieve higher compression ratios without compromising the diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied on the remaining non region of interest. The results clearly show that this hybrid strategy can yield high compression ratios with perfect reconstruction of diagnostic relevant regions, achieving high speed transmission and less storage requirement. For the sample images considered in our experiment, the compression ratio increases approximately ten times. However, this increase depends upon the size of the region of interest chosen. It is also föund that the pre-processing (contrast stretching) of region of interest improves compression ratios on mammograms but not on ultrasound images

Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images

The need for transmission and archive of mammograms and ultrasound Images has dramatically increased in tele-healthcare applications. Such images require large amount of' storage space which affect transmission speed. Therefore an effective compression scheme is essential. Compression of these images. in general. laces a great challenge to compromise between the higher compression ratio and the relevant diagnostic information. Out of the many studied compression schemes. lossless . IPl. (i- LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are chosen based on a comprehensive experimental study carried on a large number of mammograms and ultrasound images of different sizes and texture. The lossless schemes are evaluated based on the compression ratio and compression speed. The distortion in the image quality which is introduced by lossy methods evaluated based on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio (PSNR). It is found that lossless compression can achieve a modest compression ratio 2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than lossless ones but at the price of the image quality which may impede diagnostic conclusions. In this work, a new compression approach called Ilvbrid Region-based Image Compression Scheme (IIYRICS) has been proposed for the mammograms and ultrasound images to achieve higher compression ratios without compromising the diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied on the remaining non region of interest. The results clearly show that this hybrid strategy can yield high compression ratios with perfect reconstruction of diagnostic relevant regions, achieving high speed transmission and less storage requirement. For the sample images considered in our experiment, the compression ratio increases approximately ten times. However, this increase depends upon the size of the region of interest chosen. It is also föund that the pre-processing (contrast stretching) of region of interest improves compression ratios on mammograms but not on ultrasound images

Correlation modeling for compression of computed tomography images

Abstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this work, we propose a novel analysis which accurately predicts when the use of a multi-component transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multi-component transforms are appropriate for images with correlation coefficient r in excess of 0.87

Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images

Altres ajuts: This work has been funded by the EU Marie Curie CIG Programme under Grant PIMCO, the Engineering and Physical Sciences Research Council (EPSRC), UKThe use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes

Improved compressed sensing algorithm for sparse-view CT

In computed tomography (CT) there are many situations where reconstruction may need to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to the limited sampling rate, compromising image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total variation (TV)-base compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we formulate the problem of CT imaging under transform sparsity and sparse-view constraints, and propose a novel compressed sensing-based algorithm for CT image reconstruction from few-view data, in which we simultaneously minimize the ℓ1 norm, total variation and a least square measure. The main feature of our algorithm is the use of two sparsity transforms: discrete wavelet transform and discrete gradient transform, both of which are proven to be powerful sparsity transforms. Experiments with simulated and real projections were performed to evaluate and validate the proposed algorithm. The reconstructions using the proposed approach have less streak artifacts and reconstruction errors than other conventional methods

Image usefulness of compressed surveillance footage with different scene contents

The police use both subjective (i.e. police staff) and automated (e.g. face recognition systems) methods for the completion of visual tasks (e.g person identification). Image quality for police tasks has been defined as the image usefulness, or image suitability of the visual material to satisfy a visual task. It is not necessarily affected by any artefact that may affect the visual image quality (i.e. decrease fidelity), as long as these artefacts do not affect the relevant useful information for the task. The capture of useful information will be affected by the unconstrained conditions commonly encountered by CCTV systems such as variations in illumination and high compression levels. The main aim of this thesis is to investigate aspects of image quality and video compression that may affect the completion of police visual tasks/applications with respect to CCTV imagery. This is accomplished by investigating 3 specific police areas/tasks utilising: 1) the human visual system (HVS) for a face recognition task, 2) automated face recognition systems, and 3) automated human detection systems. These systems (HVS and automated) were assessed with defined scene content properties, and video compression, i.e. H.264/MPEG-4 AVC. The performance of imaging systems/processes (e.g. subjective investigations, performance of compression algorithms) are affected by scene content properties. No other investigation has been identified that takes into consideration scene content properties to the same extend. Results have shown that the HVS is more sensitive to compression effects in comparison to the automated systems. In automated face recognition systems, `mixed lightness' scenes were the most affected and `low lightness' scenes were the least affected by compression. In contrast the HVS for the face recognition task, `low lightness' scenes were the most affected and `medium lightness' scenes the least affected. For the automated human detection systems, `close distance' and `run approach' are some of the most commonly affected scenes. Findings have the potential to broaden the methods used for testing imaging systems for security applications

Stochastic dynamics and wavelets techniques for system response analysis and diagnostics: Diverse applications in structural and biomedical engineering

In the first part of the dissertation, a novel stochastic averaging technique based on a Hilbert transform definition of the oscillator response displacement amplitude is developed. In comparison to standard stochastic averaging, the requirement of “a priori” determination of an equivalent natural frequency is bypassed, yielding flexibility in the ensuing analysis and potentially higher accuracy. Further, the herein proposed Hilbert transform based stochastic averaging is adapted for determining the time-dependent survival probability and first-passage time probability density function of stochastically excited nonlinear oscillators, even endowed with fractional derivative terms. To this aim, a Galerkin scheme is utilized to solve approximately the backward Kolmogorov partial differential equation governing the survival probability of the oscillator response. Next, the potential of the stochastic averaging technique to be used in conjunction with performance-based engineering design applications is demonstrated by proposing a stochastic version of the widely used incremental dynamic analysis (IDA). Specifically, modeling the excitation as a non-stationary stochastic process possessing an evolutionary power spectrum (EPS), an approximate closed-form expression is derived for the parameterized oscillator response amplitude probability density function (PDF). In this regard, IDA surfaces are determined providing the conditional PDF of the engineering demand parameter (EDP) for a given intensity measure (IM) value. In contrast to the computationally expensive Monte Carlo simulation, the methodology developed herein determines the IDA surfaces at minimal computational cost. In the second part of the dissertation, a novel multiple-input/single-output (MISO) system identification technique is developed for parameter identification of nonlinear and time-variant oscillators with fractional derivative terms subject to incomplete non-stationary data. The technique utilizes a representation of the nonlinear restoring forces as a set of parallel linear sub-systems. Next, a recently developed L1-norm minimization procedure based on compressive sensing theory is applied for determining the wavelet coefficients of the available incomplete non-stationary input-output (excitation-response) data. Several numerical examples are considered for assessing the reliability of the technique, even in the presence of incomplete and corrupted data. These include a 2-DOF time-variant Duffing oscillator endowed with fractional derivative terms, as well as a 2-DOF system subject to flow-induced forces where the non-stationary sea state possesses a recently proposed evolutionary version of the JONSWAP spectrum. In the third part of this dissertation, a joint time-frequency analysis technique based on generalized harmonic wavelets (GHWs) is developed for dynamic cerebral autoregulation (DCA) performance quantification. DCA is the continuous counter-regulation of the cerebral blood flow by the active response of cerebral blood vessels to the spontaneous or induced blood pressure fluctuations. Specifically, various metrics of the phase shift and magnitude of appropriately defined GHW-based transfer functions are determined based on data points over the joint time-frequency domain. The potential of these metrics to be used as a diagnostics tool for indicating healthy versus impaired DCA function is assessed by considering both healthy individuals and patients with unilateral carotid artery stenosis. Next, another application in biomedical engineering is pursued related to the Pulse Wave Imaging (PWI) technique. This relies on ultrasonic signals for capturing the propagation of pressure pulses along the carotid artery, and eventually for prognosis of focal vascular diseases (e.g., atherosclerosis and abdominal aortic aneurysm). However, to obtain a high spatio-temporal resolution the data are acquired at a high rate, in the order of kilohertz, yielding large datasets. To address this challenge, an efficient data compression technique is developed based on the multiresolution wavelet decomposition scheme, which exploits the high correlation of adjacent RF-frames generated by the PWI technique. Further, a sparse matrix decomposition is proposed as an efficient way to identify the boundaries of the arterial wall in the PWI technique

Compressed Sensing Accelerated Magnetic Resonance Spectroscopic Imaging

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future