Search and Implementation of Optimization Algorithms in Analysis of Ultrasonic Pictures in Neurology
This contribution deals with search and implementation of optimization algorithms in analyzing and evaluating objects of interest present in ultrasound images as well as assessing the progress or regressions illustrated in these objects. These objects are highly significant from a medical perspective and include atherosclerotic plaque in carotid arteries, the intima-media thickness in the distal part of the common carotid artery, cerebral cortex size and brain stem findings in cases of Parkinson disease. Here, we describe procedures employing common principles and methods for recognizing points of interest in images that may serve in finding and determining pixel coordinates and other parameters and properties of analyzed objects. We use the stochastic optimization algorithm to optimize the energy function of deformable models used to approximate the locations and shapes of object boundaries in medical images. We suppose that evolutionary algorithm called SOMA can be used to find the desired global solution. Evolutionary algorithms are based on principles of evolution found in nature and respect the Darwin‘s theory of natural selection according to the defined cost function and gene recombination and mutation. As the computation of gradient vector flow field and also the evolution of active contour are computationally very expensive, we investigate the suitability of the GPU for a parallel implementation.
KeywordsBrain Stem Common Carotid Artery Ultrasound Image Active Contour Gradient Vector
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