Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Segmentation of MRI Prostate Images using Gaussian Mixture Models (GMMs)

Roslee, Nur Aqilah (2016) Segmentation of MRI Prostate Images using Gaussian Mixture Models (GMMs). IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Restricted to Registered users only

Download (2356Kb)


Image processing is advancing rapidly as data acquisition through digital medium is becoming more common. The advancement of data acquisition directly affects the medical field, specifically in terms of processing the images produced by Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), ultrasound, and XRay. These wide range of imaging modalities would need a faster, more reliable algorithm for image segmentation to cater for the different characteristics and needs of image processing. Automated segmentation approach is extensively studied as the conventional approach, which is analysing the images manually, has been proven to be very time consuming besides being susceptible to human errors. In this paper, the performance of Gaussian Mixture Model method for medical image segmentation is assessed with the help of Expectation-Maximization (EM) method for data training and Bayesian Information Criterion (BIC) function to prevent data overfitting. This method is evaluated against MRI prostate images in particular. This method is also compared to K-Means algorithm to evaluate its performance with synthetic image and also clinical MRI image.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 01 Mar 2017 11:38
Last Modified: 01 Mar 2017 16:32
URI: http://utpedia.utp.edu.my/id/eprint/17191

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...