MR Brain Image Segmentation Using Spatial Fuzzy C- Means Clustering Algorithm

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation.

Analisis Komputasi pada Segmentasi Citra Medis Adaptif Berbasis Logika Fuzzy Teroptimasi

Abstract 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-mean

Implementasi Algoritma Fuzzy C Means dan Stastitical Region Merging Pada Segmentasi Citra

Segmentasi citra berbasis clustering pada penelitian ini menggunakan metode Fuzzy C Means dengan menerapkan fungsi objektif Xie Beni Index. Preprocessing diterapkan pada model yang dikembangkan ini menggunakan metode Statistical Region Merging. Spatial function diterapkan pada metode Fuzzy C Means untuk mengurangi noise pada saat clustering. Evaluasi sistem dilakukan dengan pengukuran nilai cluster validity (Xie Beni Index), waktu eksekusi, dan jumlah iterasi. Hasil pengujian pada tiga buah citra uji menunjukan metode yang diusulkan mampu melakukan segmentasi citra dengan baik. &nbsp

Segmentasi Citra dengan Menggunakan Metode Fuzzy C-Means

Segmentasi citra adalah proses mempartisi citra menjadi beberapa daerah atau objek. Metode clustering merupakan salah satu metode yang digunakan untuk memecahkan masalah ini. Metode clustering dapat membedakan setiap daerah sesuai dengan tingkat keabuan citra. Salah satu metode clustering yang sering digunakan untuk menyelesaikan masalah segmentasi citra adalah Fuzzy C-Means (FCM). Metode FCM memiliki kelebihan yaitu dapat menentukan pusat cluster lebih tepat. Pada skripsi ini dibahas bagaimana metode FCM digunakan untuk menyelesaikan masalah segmentasi citra. Citra disegmentasi dalam ruang warna Hue Saturation Value (HSV) dan ruang warna Red Green Blue (RGB). Percobaan dilakukan dengan membandingkan hasil segmentasi citra menggunakan metode FCM dalam ruang warna HSV dan RGB. Hasil percobaan menunjukkan bahwa metode FCM dalam ruang warna HSV dapat memberikan hasil yang lebih baik dibandingkan dengan ruang warna RGB. Namun, terkadang segmentasi citra memberikan hasil yang kurang memuaskan pada citra grayscale

Spatial Information Based Image Segmentation Using a Modified Particle Swarm Optimization Algorithm

This article proposes a particle swarm based segmentation algorithm for automatically grouping the pixels of an image into different homogeneous regions. In contrast to most of the existing evolutionary image segmentation techniques, we have incorporated spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The two very important advantages of the new method are: 1) It does not require a priori knowledge of the number of partitions in the image and 2) It yields regions, more homogeneous than the existing methods even in presence of noise. 1