Knowledge-based classification and tissue labeling of magnetic resonance images of human brain

1993 ◽  
Author(s):  
ChunLin Li ◽  
Lawrence O. Hall ◽  
Dmitry B. Goldgof
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Knowledge Based

Lipsia—a new software system for the evaluation of functional magnetic resonance images of the human brain

2001 ◽  
Vol 25 (6) ◽  
pp. 449-457 ◽  
Author(s):  
Gabriele Lohmann ◽  
Karsten Müller ◽  
Volker Bosch ◽  
Heiko Mentzel ◽  
Sven Hessler ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Software System ◽  
Functional Magnetic Resonance

scholarly journals A Knowledge-Based Approach for Carpal Tunnel Segmentation from Magnetic Resonance Images

2012 ◽  
Vol 26 (3) ◽  
pp. 510-520 ◽  
Author(s):  
Hsin-Chen Chen ◽  
Yi-Ying Wang ◽  
Cheng-Hsien Lin ◽  
Chien-Kuo Wang ◽  
I-Ming Jou ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Carpal Tunnel ◽  
Magnetic Resonance Images ◽  
Knowledge Based

scholarly journals Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

2019 ◽  
Vol 46 (10) ◽  
pp. 4405-4416 ◽  
Author(s):  
Erik Verburg ◽  
Jelmer M. Wolterink ◽  
Stephanie N. Waard ◽  
Ivana Išgum ◽  
Carla H. Gils ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Chest Wall ◽  
Magnetic Resonance Images ◽  
Dense Breasts ◽  
Knowledge Based

AN INTERACTIVE CBIR SYSTEM BASED ON ANFIS LEARNING SCHEME FOR HUMAN BRAIN MAGNETIC RESONANCE IMAGES RETRIEVAL

2012 ◽  
Vol 24 (01) ◽  
pp. 27-36 ◽  
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Image Retrieval ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Query Image ◽  
Fuzzy Classifier ◽  
Inference System

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

scholarly journals Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1402
Author(s):  
Zhao Zhang ◽  
Guangfei Li ◽  
Yong Xu ◽  
Xiaoying Tang
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Autism Spectrum ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Psychiatric Diseases ◽  
Hyperactivity Disorder ◽  
Future Research Directions ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

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