Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images

2012 ◽  
Author(s):  
Syoji Kobashi ◽  
Jayaram K. Udupa
Keyword(s):  
Image Segmentation ◽  
Object Model ◽  
Mr Images ◽  
Fuzzy Connectedness ◽  
Model Based ◽  
Newborn Brain ◽  
Brain Mr Images

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 487-496
Author(s):  
Li Liu ◽  
Chi Hua ◽  
Zixuan Cheng ◽  
Yunfeng Ji
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Automatic Segmentation ◽  
Learning Ability ◽  
Mr Images ◽  
Feature Maps ◽  
Intelligent Diagnosis ◽  
Brain Mr Images ◽  
Self Organizing

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.

An Approach with Neural Network to Detection of MRI Anatomy

1994 ◽  
Vol 7 (1) ◽  
pp. 47-52
Author(s):  
R. De Blasi ◽  
A. Blonda ◽  
G. Pasquariello ◽  
D. Milella ◽  
F. Dicuonzo ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Back Propagation ◽  
Learning Rule ◽  
Object Classification ◽  
Modular System ◽  
Training Phase ◽  
Self Organizing Map ◽  
Mr Images ◽  
Medical Expert ◽  
Brain Mr Images

In this paper an artificial modular system applied to object classification in brain MR images is presented. It consists of two modules based on neural architectures joined in sequence to perform first an image segmentation and then an object classification. For these two steps a Self Organizing Map and a Multilayer Perceptron trained with the Back-Propagation learning rule have been used. The objective of the system is the automatic recognition of the anatomic structures in MR images of the cerebral section passing through the orbits and the visual pathways. To reach this goal we have submitted the two networks to a training phase realized by an unsupervised process for the image segmentation and by a supervised process for regions labelling. This last step has been based on topographic relations supplied by a medical expert. The system has been useful to discriminate 20 different classes of anatomic objects over the considered section. Preliminary results are presented.

scholarly journals Image Segmentation of Brain MR Images Using Otsu’s based Hybrid WCMFO Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 681-700 ◽  
Author(s):  
A. Renugambal ◽  
K. Selva Bhuvaneswari
Keyword(s):  
Image Segmentation ◽  
Mr Images ◽  
Brain Mr Images
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