RESUMEN
For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
Asunto(s)
Humanos , Algoritmos , Inteligencia Artificial , Aumento de la Imagen , Métodos , Interpretación de Imagen Asistida por Computador , Métodos , Imagenología Tridimensional , Métodos , Imagen por Resonancia Magnética , Métodos , Neoplasias Meníngeas , Diagnóstico , Patología , Meningioma , Diagnóstico , Patología , Reconocimiento de Normas Patrones Automatizadas , MétodosRESUMEN
To propose an optimal level set approach for fast medical image segmentation. By confining the computation quantity of the level sets function and using the image characteristics, we improved the efficiency of segmentation and decreased the parameter setting in some degree for DSA vascular segmentation.