Abstract
Fuzzy C-means (FCM) has been widely applied to segmentation of medical images, especially MRI images for identifying living organs and supporting medical diagnosis. However, in practice, this method is too sensitive to image noises. Then, many methods have been proposed to improve the objective function of FCM by adding a penalty term to it. One drawback of these methods is that they can determine neither the appropriate size of observation window for each pixel of interest for incorporating spatial information, nor the suitable importance coefficient of the penalty term. Moreover, the modification of the objective function of FCM often causes additional complex derivations. In this paper, we develop a new FCM-based method for medical MRI image segmentation. This method permits to dynamically determine the optimal size of observation window for each pixel of interest without adding any penalty term. Moreover, a n-dimensional feature vector including both local and global spatial information between neighboring pixels is generated to describe each pixel in the objective function. And specialized a priori knowledge is integrated into the segmentation procedure in order to control the application of FCM for tissue classification of thigh. The effectiveness and the robustness of the proposed method have been validated by real MRI image of thigh.
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Antonio, P. (2016). FCM-Based Method for MRI Segmentation of Anatomical Structure. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_16
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DOI: https://doi.org/10.1007/978-3-319-31744-1_16
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