Human Lesion Detection Method Based on Image Information and Brain Signal

• Published in: IEEE access Vol. 7; pp. 11533 - 11542
• Main Authors: Li, Gongfa, Jiang, Du, Zhou, Yanling, Jiang, Guozhang, Kong, Jianyi, Manogaran, Gunasekaran
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; applicability method; Brain; brain signal; Cancer; Central nervous system; Clustering algorithms; Computed tomography; Image processing; Image segmentation; improved region growing algorithm; Liver; Medical diagnostic imaging; Medical image segmentation; Medical imaging; Organs; Post-processing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2891749

The brain is the largest and most complex structure in the central nervous system. It dominates all activities in the body, and the lesions in the human body are also reflected in the brain signal. In this paper, the image method is used to assist the brain signal to detect the human lesion. Due to the particularity of medical images, there is no common segmentation method for any medical image, and there is no objective standard to judge whether the segmentation is effective. Medical image segmentation technology is still a bottleneck restricting the development and the application of other related technologies in medical image processing. Based on the above reasons, this paper proposes an improved region growing algorithm based on the fuzzy theory and region growing algorithm. The algorithm is used to segment the medical images of the liver and chest X-ray of different human organs. The improved algorithm uses a threshold segmentation algorithm to assist in the automatic selection of seed points and improves the region growing rules, then morphological post-processing is used to improve the segmentation effect. The experimental results show that the improved region growing algorithm has better segmentation effect under two different organs, which proves that the algorithm has certain applicability, and its accuracy and segmentation quality are better than the traditional region growing algorithm. This algorithm combines the advantages of the threshold method and traditional region growing method. It is feasible in algorithm and has certain application value.