Employing HSOFM Neural Network and FCM To Extract Liver's Abnormal Regions In MRI and CT Scan Images

Background: A Liver tumor is a dangerous disease that may leads to death, the chances of survival will increase when it is detected in early time.
Materials and Methods: In this study, clustering Fuzzy c-mean and ''Self-Organization Feature Map'', HSOFM, which is an unsupervised artificial neural network, based on the histogram of the image, are presented for segmenting, isolating, and then extracting tumors and other abnormal regions in liver images of MRI and CT imaging. In an additional processing, morphological operations were used to achieve the complete final extraction of the isolated regions without any extra pixels that do not belong to the abnormal regions. These two methods were applied on five MRI and four CT scan images. The extracted regions surface areas were calculated and compared with the groundtruth, manually extracted mass regions, to check the goodness adequacy of the adopted methods. The work was achieved by Mat Lab programing environment.
Results: The percent relative differences of the extracted abnormal regions by implementing the adopted methods with the groundtruth were ranged from 1.108 % to 3.861 % for the images of MRI, while for CT scan images, the percent relative differences were in the range from 0.732 % to 3.456%. By implementing FCM clustering, the percent relative difference ranged from 0.724% to 4.370 % for three MRI images, while for CT scan images, the percent relative difference was 4.327 % for one of the adopted CT images. Results of implementing HSOFM indicate the high-quality performance of this method with 95 % accuracy.
Conclusion: From the results we can conclude that there is an appropriate number of nodes and clusters that is a more adequate choice than others depending on the properties’ intensity variance in each processed image. The comparison between the two implemented methods figures out the superiority of the HSOFM method over FCM. There are some limitations, such as the small size of the abnormal regions as well as the interference of the abnormal regions with the normal regions of the similar intensity that require applying advanced enhancement methods as an additional preprocessing step.