Detection of blood vessels in human brain 3D magnetic resonance images with the use of mathematical morphology and region growing algorithms

2009 ◽  
Author(s):  
Adam Sankowski ◽  
Andrzej Materka
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Blood Vessels ◽  
Mathematical Morphology ◽  
Region Growing ◽  
Magnetic Resonance Images

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

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.

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