Abou-Bakr M. Youssef

Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion

This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.

Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images

Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.

Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographers experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

Characterisation of electrocardiogram signals based on blind source separation

Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.

Automated measurements in obstetric ultrasound images

Measurement of the fetal head circumference and diameters in addition to the femur length is crucial for the estimation of fetal age and growth pattern. Due to the noisy nature of ultrasound images and variation in image acquisition and measurement techniques, manual measurements of these parameters are subject to inter and intra-observer variability. The main objective of this work is to apply morphologically-based algorithms in order to recognize the fetal head contour in the ultrasound image, refine its shape and compensate for different irregularities, then correctly measure its dimensions, thus attaining accuracy and reproducibility of measurements. The automation algorithms utilize morphological operations, Hough transforms, and tracing methods. Measurements performed using the automation algorithms were closely correlated to those obtained manually and have been verified using many cases. Automation algorithms can be further improved to be performed online and can be implemented in the ultrasound machine, thus achieving consistency of fetal measurements.

A new real-time retinal tracking system for image-guided laser treatment

A new system is proposed for tracking sensitive areas in the retina for computer-assisted laser treatment of choroidal neovascularization (CNV). The system consists of a fundus camera using red-free illumination mode interfaced to a computer that allows real-time capturing of video input. The first image acquired is used as the reference image and utilized by the treatment physician for treatment planning. A grid of seed contours over the whole image is initiated and allowed to deform by splitting and/or merging according to preset criteria until the whole vessel tree is demarcated. Then, the image is filtered using a one-dimensional Gaussian filter in two perpendicular directions to extract the core areas of such vessels. Faster segmentation can be obtained for subsequent images by automatic registration to compensate for eye movement and saccades. An efficient registration technique is developed whereby some landmarks are detected in the reference frame then tracked in the subsequent frames. Using the relation between these two sets of corresponding points, an optimal transformation can be obtained. The implementation details of proposed strategy are presented and the obtained results indicate that it is suitable for real-time location determination and tracking of treatment positions.

Floating Navigator Echo (FNAV) for In-Plane 2D Translational Motion Estimation

A modification of the classical navigator echo (NAV) technique is presented whereby both 2D translational motion components are computed from a single navigator line. Instead of acquiring the NAV at the center of the k‐space, a kx line is acquired off‐center in the phase‐encoding (ky) direction as a floating NAV (FNAV). It is shown that the translational motion in both the readout and phase‐encoding directions can be computed from this line. The algorithm used is described in detail and verified experimentally. The new technique can be readily implemented to replace classic NAV in MRI sequences, with little to no additional cost or complexity. The new method can help suppress 2D translational motion and provide more accurate motion estimates for other motion‐suppression techniques, such as the diminishing variance algorithm. Magn Reson Med 51:403–407, 2004.

Real time adaptive ultrasound speckle reduction and coherence enhancement

We present a novel approach for speckle reduction and coherence enhancement of ultrasound images. The algorithm maximally low-pass filters those parts of the image which correspond to fully developed speckle, while substantially preserving information associated with resolved-object structure. The proposed algorithm is based on coherent anisotropic diffusion with an efficient discretization scheme that could be used as a preprocessing step for online visualization of ultrasound images even when it is implemented on a PC based system. It is shown experimentally that this technique produces superior results when compared to the results obtained from similar methods.

Improved Harmonic Phase (HARP) Method for Motion Tracking a Tagged Cardiac MR images

The diagnosis of cardiovascular disease requires precise assessment of both morphology and function of the heart. Currently, magnetic resonance imaging (MRI) provides a useful tool for accurate and reproducible assessment of regional function of the left ventricle noninvasively. MR tagging produces images of the heart that can be analyzed using harmonic phase (HARP) method to describe the regional function of the heart. In order to calculate regional function, a circular mesh is manually built at a specific timeframe, after which the points of such mesh are tracked using the HARP technique. The tracking is not perfect and some individual points on the mesh could fail in tracking. In this work, a new method is presented to improve the tracking by combining HARP with active contour methods. This modified HARP technique is more robust than the previous HARP technique