Magnetic resonance equipment for medical imaging

2011 ◽  
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

scholarly journals A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image

2018 ◽  
Vol 3 (3) ◽  
pp. 285 ◽  
Author(s):  
Shaik Basheera ◽  
MSatya Sai Ram
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Brain Tumors ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Independent Component ◽  
Segmentation Evaluation ◽  
Daunting Task

<p>Medical imaging and analysis plays a crucial role in diagnosis and treatment planning. The anatomical complexity of human brain makes the process of imaging and analyzing very difficult. In spite of huge advancements in medical imaging procedures, accurate segmentation and classification of brain abnormalities remains a challenging and daunting task. This challenge is more visible in the case of brain tumors because of different possible shapes of tumors, locations and image intensities of different types of tumors. In this paper we have presented a method for automated segmentation of brain tumors from magnetic resonance images. An enhanced and modified Gaussian mixture mode model and the independent component analysis segmentation approach has been employed for segmenting brain tumors in magnetic resonance images. The results of segmentation are validated with the help of segmentation evaluation parameters.</p>

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