Modified Texture, Intensity and Orientation
Constraint Based Region Growing Segmentation of
2D MR Brain Tumor Images
Angel
Viji1 and J. Jayakumari2
1Department
of Computer Science and Engineering, Noorul Islam University, India
2Department
of Electronic and Communication, Noorul Islam University, India
Abstract:
Image
segmentation is a process of dividing an image into different regions such that
each region is nearly homogeneous. Magnetic Resonance (MR) images always
contain a significant amount of noise caused by operator performance, equipment
and the environment which can lead to serious inaccuracies with segmentation.
Radiologists perform diagnosis manually at early stage. It is a very
challenging and difficult task for radiologists to correctly classify the
abnormal regions in the brain tissue, because Magnetic Resonance Images (MRI) images
are noisy images. Because the tumors are inhomogeneous, un-sharp and faint, but
show an intensity pattern that is different from the adjacent healthy tissue, a
segmentation based on intensity, orientation and texture properties is proposed
here. With this approach the image
segmentation problem can be formulated and solved in a principled way based on
well-established mathematical theories. The image clustering using texture also
reduces the sensitivity to noise and results in enhanced image segmentation
performance. The ground truth of the tumor boundaries is manually extracted from publicly
available sources. Experimental
results show that our method is robust and more accurate than other well known
models. The superiority of the proposed method is examined and demonstrated
through a large number of experiments using MR images.
Keywords:
Segmentation, MRI images, texture, region growing.
Received October 4, 2013; accepted April 28, 2014