Dr. Mahmoud A. Abdallah Profile

Dr. Mahmoud A. Abdallah

at Central State Univ

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Proceedings Article | 24 February 2012 Paper

SinoCor: motion correction in SPECT

Debasis Mitra, Daniel Eiland, Mahmoud Abdallah, Rostyslav Bouthcko, Grant Gullberg, Norberto Schechtmann

Proceedings Volume 8314, 831452 (2012) https://doi.org/10.1117/12.911101

KEYWORDS: Single photon emission computed tomography, Head, Reconstruction algorithms, Motion detection, Sensors, Detection and tracking algorithms, Image quality, Heart, Motion estimation, Nuclear imaging

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Motion is a serious artifact in Cardiac nuclear imaging because the scanning operation takes a long time. Since reconstruction algorithms assume consistent or stationary data the quality of resulting image is affected by motion, sometimes significantly. Even after adoption of the gold standard MoCo(R) algorithm from Cedars-Sinai by most vendors, heart motion remains a significant challenge. Also, any serious study in quantitative analysis necessitates correction for motion artifacts. It is generally recognized that human eye is a very sensitive tool for detecting motion. However, two reasons prevent such manual correction: (1) it is costly in terms of specialist's time, and (2) no such tool for manual correction is available currently. Previously, at SPIE-MIC'11, we presented a simple tool (SinoCor) that allows sinograms to be corrected manually or automatically. SinoCor performs correction of sinograms containing inter-frame patient or respiratory motions using rigid-body dynamics. The software is capable of detecting the patient motion and estimating the body-motion vector using scanning geometry parameters. SinoCor applies appropriate geometrical correction to all the frames subsequent to the frame when the movement has occurred in a manual or automated mode. For respiratory motion, it is capable of automatically smoothing small oscillatory (frame-wise local) movements. Lower order image moments are used to represent a frame and the required rigid body movement compensation is computed accordingly. Our current focus is on enhancement of SinoCor with the capability to automatically detect and compensate for intra-frame motion that causes motion blur on the respective frame. Intra-frame movements are expected in both patient and respiratory motions. For a controlled study we also have developed a motion simulator. A stable version of SinoCor is available under license from Lawrence Berkeley National Laboratory.

Proceedings Article | 3 October 1995 Paper

Automatic target identification using neural networks

Mahmoud Abdallah, Tayib Samu, William Grissom

Proceedings Volume 2588, (1995) https://doi.org/10.1117/12.222708

KEYWORDS: Neural networks, Feature extraction, Target recognition, System identification, Image processing, Detection and tracking algorithms, Image processing software, MATLAB, Fourier transforms, Algorithm development

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Neural network theories are applied to attain human-like performance in areas such as speech recognition, statistical mapping, and target recognition or identification. In target identification, one of the difficult tasks has been the extraction of features to be used to train the neural network which is subsequently used for the target's identification. The purpose of this paper is to describe the development of an automatic target identification system using features extracted from a specific class of targets. The extracted features were the graphical representations of the silhouettes of the targets. Image processing techniques and some Fast Fourier Transform (FFT) properties were implemented to extract the features. The FFT eliminates variations in the extracted features due to rotation or scaling. A Neural Network was trained with the extracted features using the Learning Vector Quantization paradigm. An identification system was set up to test the algorithm. The image processing software was interfaced with MATLAB Neural Network Toolbox via a computer program written in C language to automate the target identification process. The system performed well as at classified the objects used to train it irrespective of rotation, scaling, and translation. This automatic target identification system had a classification success rate of about 95%.

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