Paper Info
Title
Intelligent Diagnosis Method Of Mri Brain Image Using Parallel Self-Organizing Feature Maps Neural Network
Abstract
Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.
Year
DOI
Venue
2021
10.1166/jmihi.2021.3285
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Keywords
DocType
Volume
Brain MRI, Image Segmentation, Intelligent Diagnosis, Self-Organizing Feature Maps
Journal
11
Issue
ISSN
Citations 
2
2156-7018
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Li Liu111.36
Chi Hua200.34
Zixuan Cheng300.34
Yunfeng Ji485.19