Abstract:
This study was aimed to segment and classify the normal liver in MR image.
Using pixel intensity and texture analysis to classify and then segment the liver
using the classified image then further classify the segmented liver using
textural feature. The MRI system used Philips intera 1.5 Tesla and the data
collected randomly from 50 patients out of 200. The classification and
segmentation processes were carried out using Interactive Data Language (IDL)
program as platform for the generated codes. The results of classification were
fed to SPSS software to find the classification score for further classification.
The total classification accuracy of the segmented liver with it is associated
component was 94.3%, with 100% classification for liver tissue, IVC and, while
ligament, portal and hepatic vein was 85.7% for each. The most discernible
features were the mean intensity feature and the signal feature. In conclusion
the applied algorithm showed a potential success to adopt such procedure in
medical image processing
