ANALISIS KEKEBALAN METODE PENENTUAN LOKASI DAN KUANTIFIKASI DEGENERASI OTAK BERBASIS GAUSSIAN MIXTURE MODEL TERHADAP DERAU - , DANI WULANSARI; , Dr. Agfianto Eko Putra, M.Si. ([Yogyakarta] : Universitas Gadjah Mada, 2014)
In medicine, there are diseases such as Alzheimer's are not yet found a way of healing. But one thing is believed to be the cause of brain degeneration or reduced levels of human brain tissue. Diagnosis is expected to continue to find ways to prevent or slow down the degeneration of brain distribution. The data used in the form of MRI, which requires a corresponding image processing techniques to detect, locate and quantify the lost tissue in the early commencement of the disease. Levels of brain tissue loss that can distinguish the normal brain. The factors underlying the development of the program of determining the location and quantification of brain degeneration with Gaussian Mixture Model method. In the present study, the authors conducted immunity testing method of Gaussian Mixture Models to the noise on the program that has been developed previously in order to test the feasibility of a program to be realized to be used in the medical community. The method has three stages, these are the stage of Gaussian Mixture Models, Hidden Gaussian Mixture Models, as well as localization and quantification. How the test is to provide a noise in the images to be processed and executed on the method, and compare the MSE degeneration ROI in the image without noise and image noise are given. Noise used in this study is the noise that often arise in medical images. Because of the problems that may occur is the appearance of noise in the image, thereby disrupting the process to be executed The conclusion of this study, which tested the method does not have immunity to noise, after comparing the ROI degeneration MSE on images without noise and noises that turned out to have much. While the comparison of the MSE on the results of each stage of the method shows that Gaussian Mixture Models phases have the weakest immune.