Segmentation of magnetic resonance image using fractal dimension

Joseph K. K. Yau; Sau-hoi Wong; Kwok-Leung Chan

doi:10.1117/12.274101

25 April 1997 Segmentation of magnetic resonance image using fractal dimension

Joseph K. K. Yau, Sau-hoi Wong, Kwok-Leung Chan

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274101
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

In recent years, much research has been conducted in the three-dimensional visualization of medical image. This requires a good segmentation technique. Many early works use first-order and second-order statistics. First-order statistical parameters can be calculated quickly but their effectiveness is influenced by many factors such as illumination, contrast and random noise of the image. Second-order statistical parameters, such as spatial gray level co-occurrence matrices statistics, take longer time to compute but can extract the textural information. In this investigating, two different parameters, namely the entropy and the fractal dimension, are employed to perform segmentation of the magnetic resonance images of the head of a male cadaver. The entropy is calculated from the spatial gray level co-occurrence matrices. The fractal dimension is calculated by the reticular cell counting method. Several regions of the human head are chosen for analysis. They are the bone, gyrus and lobe. Results show that the parameters are able to segment different types of tissue. The entropy gives very good result but it requires very long computation time and large amount of memory. The performance of the fractal dimension is comparable with the entropy. It is simple to estimate and demands lesser memory space.

Citation Download Citation

Joseph K. K. Yau, Sau-hoi Wong, and Kwok-Leung Chan "Segmentation of magnetic resonance image using fractal dimension", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274101

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available