A New Approach for Biomedical Image Segmentation: Combined Complex-Valued Artificial Neural Network Case Study: Lung Segmentation on Chest CT Images

The principal goal of the segmentation process is to partition an image into classes or subsets that are homogeneous with respect to one or more characteristics or features. In medical imaging, segmentation is important for feature extraction, image measurements, and image display. This study presents a new version of complex-valued artificial neural networks (CVANN) for the biomedical image segmentation. Proposed new method is called as combined complex-valued artificial neural network (CCVANN) which is a combination of two complex-valued artificial neural networks. To check the validation of proposed method, lung segmentation is realized. For this purpose, we used 32 chest CT images of 6 female and 26 male patients. These images were recorded from Baskent University Radiology Department in Turkey. The accuracy of the CCVANN model is more satisfactory as compared to the single CVANN model.