Automatic Data Extraction of Liver in Computed Tomography Images Using Segmentation Technique

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.