Hybrid approach for brain tumor detection and classification in magnetic resonance images

Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

AUTOMATED SEGMENTATION AND CLASSIFICATION OF MAGNETIC RESONANCE IMAGES FOR BRAIN TUMOR DETECTION

International Journal of Research in Engineering and Technology ◽

10.15623/ijret.2016.0512012 ◽

2016 ◽

Vol 05 (12) ◽

pp. 61-63

Author(s):

Simran Kaur .

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Tumor Detection ◽

Magnetic Resonance Images ◽

Automated Segmentation

Automatic brain tumor detection in Magnetic Resonance Images

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2012.6235613 ◽

2012 ◽

Cited By ~ 18

Author(s):

Sahar Ghanavati ◽

Junning Li ◽

Ting Liu ◽

Paul S. Babyn ◽

Wendy Doda ◽

...

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Tumor Detection ◽

Magnetic Resonance Images

Magnitude Normalized and OTSU Intensity based Brain Tumor Detection Using Magnetic Resonance Images

IETE Journal of Research ◽

10.1080/03772063.2021.1982414 ◽

2021 ◽

pp. 1-11

Author(s):

V. Vinay Kumar ◽

P. Grace Kanmani Prince

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Tumor Detection ◽

Magnetic Resonance Images

Automatic Diagnosis of Brain Magnetic Resonance Images Based on Riemannian Geometry

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch062 ◽

2017 ◽

pp. 1469-1479

Author(s):

Mohamed Gouskir ◽

Belaid Bouikhalene ◽

Hicham Aissaoui ◽

Benachir Elhadadi

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Magnetic Resonance Image ◽

Tumor Detection ◽

Brain Regions ◽

Magnetic Resonance Images ◽

Automatic Diagnosis ◽

Brain Extraction ◽

Brain Magnetic Resonance Image ◽

Brain Image Analysis

Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.