|Communications on Applied Electronics
|Foundation of Computer Science (FCS), NY, USA
|Volume 7 - Number 29
|Year of Publication: 2019
|Authors: H. M. Zabir Haque
H. M. Zabir Haque . Aortic Valve Segmentation using Convolutional Neural Network with Skip Mechanism. Communications on Applied Electronics. 7, 29 ( Jun 2019), 1-5. DOI=10.5120/cae2019652819
Segmentation is a method which can be implemented inside the verge of Artificial Intelligence World. In this approach, each pixel of an image is required to be labeled to yield the final segmented result. In this paper, a novel method has been proposed which is conducted following by Convolutional Neural Network (CNN) with skip mechanisms for Segmentation. In this method, the original 3D medical image captured as a 2D slice to pass through multiple image channels along with Ground Truth in the last channel which work as an input of CNN. This sub-sample, however, gradually generate the segmentation mask for the corresponding input image. The proposed methods were tested to perform segmentation for the CT image of the human organ (Aortic Valve) which show a significant amount of accuracy with very few numbers of dataset. Here, the result has been compared with existing methods. Such a system, hence, will support many experiments to help better understanding of Humankind in the perspective of Artificial Visualization.