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DEVELOPMENT OF METHODS FOR THE PROCESSING OF MEDICAL IMAGES USING GENETIC ALGORITHMS
Abstract
In this paper we deal with analysis and evaluation of objects of interest which are present in ultrasound images as well as assessment of the progress or regressions that occurred in these objects. These objects ar e highly significant from a medicinal perspective and include atherosclerostic pl aque in carotid arte ries, 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 combination of common methods and evoluti onary algorithms for recognizing points of interest in the images that may serve in determining various parameters and properties of analyzed objects. We use the evolutionary algorithms to optimize the energy function of deformable models used to approxima te the locations and shapes of object boundaries in images. We suppose that evolutionary algorithms can be used to find the desired global solution. Evol utionary algorithms are based on principles of evolution found in nature and respect the Darwin`s theo ry of natural selection according to the defined cost function and gene recombina tion and mutation. As the computation of gradient vector flow field and also the e volution of active contour are computationally very expensive, we investig ate the suitability of the GPU for a parallel implementation. In conclusion, we compare our approach with common numerical methods on real medical images segmentation.
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