Global field of view-based pixel-level recognition method for medical images

• Published in: Journal of intelligent & fuzzy systems Vol. 45; no. 3; pp. 4009 - 4021
• Main Authors: He, Keke, Tang, Haojun, Gou, Fangfang, Wu, Jia
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 24.08.2023
• Subjects: Algorithms; Artificial intelligence; Complexity; Heterogeneity; Image processing; Image segmentation; Magnetic resonance imaging; Medical imaging; Pixels; Research facilities; Tumors
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-231053

Artificial intelligence image processing has been of interest to research investigators in tumor identification and determination. Magnetic resonance imaging for clinical detection is the technique of choice for identifying tumors because of its advantages such as accurate localization with tomography in any orientation. Nevertheless, owing to the complexity of the images and the heterogeneity of the tumors, existing methodologies have insufficient field of view and require expensive computations to capture semantic information in the view, rendering them lacking in universality of application. Consequently, this thesis developed a medical image segmentation algorithm based on global field of view attention network (GVANet). It focuses on replacing the original convolution with a transformer structure and views in a larger field-of-view domain to build a global view at each layer, which captures the refined pixel information and category information in the region of interest with fewer parameters so as to address the defective tumor edge segmentation problem. The dissertation exploits the pixel-level information of the input image, the category information of the tumor region and the normal tissue region to segment the MRI image and assign weights to the pixel representatives. This medical image recognition algorithm enables to undertake the ambiguous tumor edge segmentation task with low computational complexity and to maximize the segmentation accuracy and model property. Nearly four thousand MRI images from the Monash University Research Center for Artificial Intelligence were applied for the experiments. The outcome indicates that the approach obtains outstanding classification capability on the data set. Both the mask (IoU) and DSC quality were improved by 7.6% and 6.3% over the strong baseline.