An optimization of system for automatic recognition of ischemic stroke areas in computed tomography images

Abstract

The paper considers application of stochastic optimization to system of automatic recognition of ischemic stroke area on computed tomography (CT) images. The algorithm of recognition depends on five inputs that influence the results of automatic detection. The quality of recognition is measured by size of conjunction of ethalone image and the image calculated by the program of automatic detection. The method of Simultaneous Perturbation Stohastic Approximation algorithm with the Metropolis rule has been applied to the optimization of the quality of image recognition. The Monte-Carlo simulation experiment was performed in order to evaluate the properties of developed algorithm.

Sprendžiamas stochastin˙es aproksimacijos optimizavimo uždavinys ischeminio insulto sriˇci u automatiniam atpažinimui kompiuterin˙es tomografijos vaizduose. Atpažinimo algoritmas reguliuojamas penkiais i˙ejimo parametrais, kurie tiesiogiai itakoja atpažinimo rezultatus. Atpažinimo kokyb˙e matuojama sankirtos-s ajungos santykiu tarp etalonini u vaizd u ir atpažint u vaizd u. Opimizavimui naudojamas nuoseklios perturbacijos stochastin˙es aproksimacijos algoritmas leidžiantis optimizuoti vaizdo atpažinim a. Atlikti modeliavimo eksperimentai taikant Monte Karlo metod a, siekiant ivertinti sukurto algoritmo savybes.