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

Segmentasi Citra Adaptif Berbasis Logika Fuzzy Teroptimasi untuk Analisis Citra Medis

The success of an image analysis system depends mainly on the quality of its segmentation, where the goal of image segmentation is to separate the image into region which are meaningful for a given task. In medical imaging, this could relate to the detection of organs or tissue types from MRi images. One way of performing segmentation is by classification, in which image pixels are classified into different classes according to their respective features. In this research, the Fuzzy C-Means algorithm with spatial information is applied to segment MRi medical images. This FCM clustering utilizes the distance between each pixel and the cluster centers in the spectral domain to compute the membership function. The pixels on an object in the image are highly correlated, and this spatial information is an important characteristics which can be used to aid their labeling. The FCM method has successfully lassified the brain MRi images into five clusters, and the best representation values of the partition coefficient and partition entropy are 0.967 and 0.052 respectively

Semi Automatic Detector of Plasmodium Falciparum on Microscope Image Based

The inspection standard for diagnosis of active malaria for parasites in blood slides through a microscope. Although microscopy has good sensitivity and allows species identification and parasite counts it requires microscopy expertise and involves a labor intensive repetitive task which is time consuming. The main aim of this research is to build an automatic system for identify and classify the parasite plasmodium falciparum based on a digital image scheme. The results of this research show the color features can give higher accuracies based on the architecture of LVQ network with four hidden neuron

Principal Component Analysis Combined with Second Order Statistical Feature Method for Malaria Parasites Classification

The main challenge in detecting malaria parasites is how to identify the subset of relevant features. The objective of this study was to identify a subset of features that are most predictive of malaria parasites using second-order statistical features and principal component analysis methods. Relevant features will provide the successful implementation of the overall detection modeling, which will reduce the computational and analytical efforts. The results showed that the combination of the principal components of the feature value the correlation to the ASM, and the contrast to the correlation can separate classes of malaria parasites