Marios S. Pattichis

Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 × 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.

Foveated video quality assessment

Most image and video compression algorithms that have been proposed to improve picture quality relative to compression efficiency have either been designed based on objective criteria such as signal-to-noise-ratio (SNR) or have been evaluated, post-design, against competing methods using an objective sample measure. However, existing quantitative design criteria and numerical measurements of image and video quality both fail to adequately capture those attributes deemed important by the human visual system, except, perhaps, at very low error rates. We present a framework for assessing the quality of and determining the efficiency of foveated and compressed images and video streams. Image foveation is a process of nonuniform sampling that accords with the acquisition of visual information at the human retina. Foveated image/video compression algorithms seek to exploit this reduction of sensed information by nonuniformly reducing the resolution of the visual data. We develop unique algorithms for assessing the quality of foveated image/video data using a model of human visual response. We demonstrate these concepts on foveated, compressed video streams using modified (foveated) versions of H.263 that are standard-compliant. We rind that quality vs. compression is enhanced considerably by the foveation approach.

Foveated video compression with optimal rate control

Previously, fovcated video compression algorithms have been proposed which, in certain applications, deliver high-quality video at reduced bit rates by seeking to match the nonuniform sampling of the human retina. We describe such a framework here where foveated video is created by a nonuniform filtering scheme that increases the compressibility of the video stream. We maximize a new foveal visual quality metric. the foveal signal-to-noise ratio (FSNR) to determine the best compression and rate control parameters for a given target bit rate. Specifically, we establish a new optimal rate control algorithm for maximizing the FSNR using a Lagrange multiplier method defined on a curvilinear coordinate system. For optimal rate control, we also develop a piecewise R-D (rate-distortion)/R-Q (rate-quantization) model. A fast algorithm for searching for an optimal Lagrange multiplier lambda* is subsequently presented. For the new models, we show how the reconstructed video quality is affected, where the FSNR is maximized, and demonstrate the coding performance for H.263,+,++/MPEG-4 video coding. For H.263/MPEG video coding, a suboptimal rate control algorithm is developed for fast, high-performance applications. In the simulations, we compare the reconstructed pictures obtained using optimal rate control methods for foveated and normal video. We show that foveated video coding using the suboptimal rate control algorithm delivers excellent performance under 64 kb/s.

Time-scale analysis of motor unit action potentials

Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of neuromuscular disorders. Following the recent development of computer-aided EMC equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, the usefulness of the wavelet transform (WT), that provides a linear time-scale representation is investigated, for describing motor unit action potential (MUAP) morphology. The motivation behind the use of the WT is that it provides localized statistical measures (the scalogram) for nonstationary signal analysis. The following four WTs were investigated in analyzing a total of 800 MUAPs recorded from 12 normal subjects, 15 subjects suffering with motor neuron disease, and 13 from myopathy: Daubechies with four and 20 coefficients, Chui (CH), and Battle-Lemarie (BL). The results are summarized as follows: 1) most of the energy of the MUAP signal is distributed among a small number of well-localized (in time) WT coefficients in the region of the main spike, 2) for MUAP signals, the authors look to the low-frequency coefficients for capturing the average waveshape of the MUAP signal over long durations, and the authors look to the high-frequency coefficients for locating MUAP spike changes, 3) the Daubechies 4 wavelet, is effective in tracking the transient components of the MUAP signal, 4) the linear spline CH (semiorthogonal) wavelet provides the best MUAP signal approximation by capturing most of the energy in the lowest resolution approximation coefficients, and 5) neural network BY (DY) of Daubechies 4 and BL WT coefficients was in the region of 66%, whereas BY for the empirically determined time domain feature set was 78%. In conclusion, wavelet analysis provides a new way in describing MUAP morphology in the time-frequency plane. This method allows for the fast extraction of localized frequency components, which when combined with time domain analysis into a modular neural network decision support system enhances further the BY to 82.5% aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders.

Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering and Level Sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.

m-Health e-Emergency Systems: Current Status and Future Directions [Wireless corner]

Rapid advances in wireless communications and networking technologies, linked with advances in computing and medical technologies, facilitate the development and offering of emerging mobile systems and services in the healthcare sector. The objective of this paper is to provide an overview of the current status and challenges of mobile health systems (m-health) in emergency healthcare systems and services (e-emergency). The paper covers a review of recent e-emergency systems, including the wireless technologies used, as well as the data transmitted (electronic patient record, bio-signals, medical images and video, subject video, and other). Furthermore, emerging wireless video systems for reliable communications in these applications are presented. We anticipate that m-health e-emergency systems will significantly affect the delivery of healthcare; however, their exploitation in daily practice still remains to be achieved

Robust Multispectral Image Registration Using Mutual-Information Models

Image registration is a vital step in the processing of multispectral imagery. The accuracy to which imagery collected at multiple wavelengths can be aligned directly affects the resolution of the spectral end products. Automated registration of the multispectral imagery can often be unreliable, particularly between visible and infrared imagery, due to the significant differences in scene reflectance at different wavelengths. This is further complicated by the thermal features that exist at longer wavelengths. We develop new mathematical and computational models for robust image registration. In particular, we develop a frequency-domain model for the mutual-information surface around the optimal parameters and use it to develop a robust gradient ascent algorithm. For a robust performance, we require that the algorithm be initialized close to the optimal registration parameters. As a measure of how close we need to be, we propose the use of the correlation length and provide an efficient algorithm for estimating it. We measure the performance of the proposed algorithm over hundreds of random initializations to demonstrate its robustness on real data. We find that the algorithm should be expected to converge, as long as the registration parameters are initialized to be within the correlation-length distance from the optimum

Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images

Abstract The aim of this study was to investigate the usefulness of multilevel binary and gray scale morphological analysis in the assessment of atherosclerotic carotid plaques. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques (Stroke, Transient Ischaemic Attack (TIA), Amaurosis Fugax (AF)). We carefully develop the clinical motivation behind our approach. We do this by relating the proposed L-images, M-images and H-images in terms of the clinically established hypoechoic, isoechoic and hyperechoic classification. Normalized pattern spectra were computed for both a structural, multilevel binary morphological model, and a direct gray scale morphology model. From the plots of the average pattern spectra, it is clear that we have significant differences between the symptomatic and asymptomatic spectra. Here, we note that the morphological measurements appear to be in agreement with the clinical assertion that symptomatic plaques tend to have large lipid cores while the asymptomatic plaques tend to have small lipid cores. The derived pattern spectra were used as classification features with two different classifiers, the Probabilistic Neural Network (PNN) and the Support Vector Machine (SVM). Both classifiers were used for classifying the pattern spectra into either a symptomatic or an asymptomatic class. The highest percentage of correct classifications score was 73.7% for multilevel binary morphological image analysis and 66.8% for gray scale morphological analysis. Both were achieved using the SVM classifier. Among all features, the L-image pattern spectra, that also measure the distributions of the lipid core components (and some non-lipid components) gave the best classification results.

Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy

We develop new multiscale amplitude-modulation frequency-modulation (AM-FM) demodulation methods for image processing. The approach is based on three basic ideas: (i) AM-FM demodulation using a new multiscale filterbank, (ii) new, accurate methods for instantaneous frequency (IF) estimation, and (iii) multiscale least squares AM-FM reconstructions. In particular, we introduce a variable-spacing local linear phase (VS-LLP) method for improved instantaneous frequency (IF) estimation and compare it to an extended quasilocal method and the quasi-eigen function approximation (QEA). It turns out that the new VS-LLP method is a generalization of the QEA method where we choose the best integer spacing between the samples to adapt as a function of frequency. We also introduce a new quasi-local method (QLM) for IF and IA estimation and discuss some of its advantages and limitations. The new IF estimation methods lead to significantly improved estimates. We present different multiscale decompositions to show that the proposed methods can be used to reconstruct and analyze general images.

Analyzing Image Structure by Multidimensional Frequency Modulation

We develop a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images. We begin with the widely accepted definition of the instantaneous frequency vector (IF) as the gradient of the phase and define the instantaneous frequency gradient tensor (IFGT) as the tensor of component derivatives of the IF vector. Frequency modulation bounds are derived and interpreted in terms of the eigendecomposition of the IFGT. Using the IFGT, we derive the ordinary differential equations (ODEs) that describe image flowlines. We study the diagonalization of the ODEs of multidimensional frequency modulation on the IFGT eigenvector coordinate system and suggest that separable transforms can be computed along these coordinates. We illustrate these new methods of image pattern analysis on textured and fingerprint images. We envision that this work will find value in applications involving the analysis of image textures that are nonstationary yet exhibit local regularity. Examples of such textures abound in nature