Study of Segmentation Techniques for Medical Images

Abstract

Segmentation techniques are widely used in for the application of Medical Images. These
techniques play a crucial role by automating or facilitating the delineation of anatomical
structures and other regions of interest . Image Segmentation is particularly dicult due
to restrictions imposed by biological variation and image acquisition . Goal of medical
image segmentation is to perform operations on medical images to identify patterns in the
use of interaction and develop qualitative criteria to evaluate interactive segmentation
methods and extract information from it .Techniques available for segmentation are
specic to application and the segment of biological detail to be studied . Number of
techniques require wide use of Algorithms specically for certain segmentation procedure
of medical images . From the Medical Image processing point of view the classication
of segmentation techniques is on gray level and texture based techniques. Approaches
have been proposed to segment CT and MR Images. In the following chapters we
discuss the reviewed methods with respect to all information provided by the user and
the parameters for the computational part. Medical image segmentation techniques
require some form of expert supervision to provide accurate and consistent identication
of anatomic structures. A novel segmentation technique was developed that combines
a knowledge-based segmentation system with a contour model. This method exploits
the guidance of a higher level process to robustly perform the segmentation of kind
of anatomic structures. Knowledge about the anatomic structures to be segmented is
dened statistically in terms of probability density functions of parameters of location,
size, and image intensity. The active contour based technique outperform the standard
segmentation methods due to its capacity to fully enforce the available a priori knowledge
concerning the anatomic structure of interest. The active contour algorithm is suitable
for integration with high-level image understanding frameworks, and providing a robust
low-level segmentation tool. Studies are required to determine whether the proposed
algorithm is indeed capable of providing consistently superior segmentation.