Abstract:
Advanced techniques of image processing and analysis find widespread use in
medicine. In medical applications, image data are used to gather details regarding
the process of patient imaging whether it is a disease process or a physiological
process. Unfortunately, the presence of speckle noise in these images affects edges
and fine details which limit the contrast resolution and make diagnostic more
difficult. This experimental study was conducted in College of Medical
Radiological Science and Fadil Specialist Hospital. The sample of study was
included 50 patients. The main objective of this research was to study an accurate
liver segmentation method using a parallel computing algorithm using image
processing technique. The data analyzed by using MatLab program to enhance the
contrast within the soft tissues, the gray levels in both enhanced and unenhanced
images and noise variance. The main techniques of enhancement used in this study
were watershed Segmentation Algorithm. In this thesis, prominent constraints are
firstly preservation of image's overall look; secondly preservation of the diagnostic
content in the image and thirdly detection of small low contrast details in diagnostic
content of the image. The results of this technique was segmentation of liver
successfully based on the methods of enhance the computed tomography images.
This approach of image processing is funded on an attempt to interpret the problem
from the view of blind source separation (BSS), thus to see the liver image as a
simple mixture of (unwanted) background information, diagnostic information and
noise.
