Analisis Komputasi pada Segmentasi Citra Medis Adaptif Berbasis Logika Fuzzy Teroptimasi

Indah Soesanti(1*), Adhi Susanto(2), Thomas Sri Widodo(3), Maesadji Tjokronegoro(4)

(*) Corresponding Author


The objective of this research is to analyze the computation of medical image adaptive segmentation based on optimized fuzzy logic. The success of the image analysis system depends on the quality of the segmentation. The image segmentation is separating the image into regions that are meaningful for a given purpose. In this research, the Fuzzy C-Means (FCM) algorithm with spatial information is presented to segment Magnetic Resonance Imaging (MRI) medical images. The FCM clustering utilizes the distance between pixels and cluster centers in the spectral domain to compute the membership function. The pixels of an object in image are highly correlated, and this spatial information is an important characteristic that can be used to aid their labeling. This scheme greatly reduces the effect of noise. The FCM method successfully classifies the brain MRI images into five clusters. This technique is therefore a powerful method in computation for noisy image segmentation.
Keywords: computation analysis, MRI Medical image, adaptive image segmentation, fuzzy c-means

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