Image segmentation applications in early detection of Alzheimer’s disease using segmented corpus callosum

• Main Authors: Vattipolu, Bhargava Ramakrishna, Naidu, Bhanu C. S. K., kumar, K. Ashok
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Brain damage; Image processing; Image segmentation; Medical imaging
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0208920

Numerous approaches and practicals for detecting Alzheimer’s disease have been developed over time. Understanding the scanned images of the human brain is the initial stage in this procedure. This explain the extent to which the brain has been damaged. The “Image Segmentation” software is used to examine the brain images. To extract usable information from an image, Image Processing employs a number of algorithms. Each algorithm generates output in its own way. As a result, the efficiency of each method differs. A few image segmentation algorithms are employed to segment the photos in order to obtain the needed information, and ground truth is used to validate the results. This validation is used to compare the segmented image’s accuracy to the ground truth, indicating the algorithm’s efficiency. The research focuses on U-net techniques, as well as the results obtained utilizing these algorithms. This result served as the foundation for the creation of an algorithm for detecting Alzheimer’s disease. Because it is based on Alzheimer’s disease, the entire procedure is based on 2-D brain pictures, and the outcome will be 2-D images that will be validated. Based on the portion of the brain to be segmented, brain MRI scans in various orientations are used as input.