Turkish Journal of Electrical Engineering and Computer Sciences




Prostate cancer is the second largest cause of mortality among men. Prostate segmentation, i.e. the precise determination of the prostate region in magnetic resonance imaging (MRI), is generally used for prostate volume measurement, which can be used as a potential prostate cancer indicator. This paper presents a new fully automatic statistical model called the multiobjective active appearance model (MOAAM) for prostate segmentation in MR images. First, in the training stage, the appearance model, including the shape and texture model, is developed by applying principal component analysis to the training images, already outlined by a physician. Then noise and roughness are properly removed in the preprocessing step by Sticks filter and nonlinear filtering. This helps us provide the proper conditions for the prostate region detection. Finally, in order to detect the prostate region, a new multiobjective function is optimized using a suitable search algorithm. The proposed method has been applied to prostate images for segmenting the prostate boundaries. The evaluation results indicate that the presented method can yield a DSC value of \({87.4\pm5.00\%}\), is less sensitive to the edge information and initialization, and has a stronger capture range in comparison with existing methods.


Active shape model, active appearance model, prostate segmentation, genetic algorithm, objective function, nonlinear filtering

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