کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
535270 870335 2006 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Decomposing parameters of mixture Gaussian model using genetic and maximum likelihood algorithms on dental images
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Decomposing parameters of mixture Gaussian model using genetic and maximum likelihood algorithms on dental images
چکیده انگلیسی

We present new approaches based on Genetic Algorithms (GAs), Simulated Annealing (SA) and Expectation Maximization (EM) for determining parameters of the mixture Gaussian model. GAs are adaptive search techniques designed to search for near-optimal solutions of large-scale optimization problems with multiple local maxima. It has been shown that GAs are independent of initialization parameters and can efficiently optimize functions in large search spaces while the solution obtained by EM is a function of initial parameters. There is a relatively high likelihood of achieving sub-optimal solution, due to trapping in local maxima. In this work, we propose a combination of Genetic Algorithm with EM (Interlaced GA–EM) to improve estimation of Gaussian mixture parameters. The method uses population of mixture models, rather than a single mixture, iteratively in both GA and EM to determine Gaussian mixture parameters. To assess the performance of the proposed methods, a series of Gaussian phantoms, based on the ‘Modified Shepp–Logan’ method, were created. All proposed methods were employed to estimate the tissue parameters in each phantom and applied on Micro Computed Tomography (μCT) of dental images. The proposed method offers an accurate and stable solution for parameter estimation on Gaussian mixture models, with higher likelihood of achieving global optimal minima. Obtaining such accurate parameter estimation is a key requirement for image segmentation approach, which rely on a priori knowledge of tissue model parameters.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 27, Issue 13, 1 October 2006, Pages 1522–1536
نویسندگان
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