Paper Info
Title
Hybrid Appearance Based Disease Recognition of Human Brains
Abstract
The magnetic resonance imaging (MRI) is a diagnostic and treatment evaluation tool which is very widely used in various areas of medicine. MRI images provide very high quality images of the brain tissue and so can be used to study the brain conditions. This research paper proposes a productive technique to classify brain MRI images. Examining the MRI brain images manually is not only slow but is also error prone. In order to both speed up the process and maintain the quality of the classification we need a very high-quality classification system. In this research work, advanced classification techniques based on the well known SIFT and Gabor features are applied on brain images. From our analysis we observed that a hybrid feature derived with SIFT and Gabor features yielded a higher accuracy than Gabor features alone.
Year
DOI
Venue
2012
10.1109/IV.2012.99
IV
Keywords
Field
DocType
high-quality classification system,brain tissue,mri image,gabor feature,hybrid appearance,advanced classification technique,brain image,brain condition,mri brain image,high quality image,human brains,brain mri image,disease recognition,visualization,bioinformatics,magnetic resonance imaging,vectors,pca,feature extraction,image processing,histograms,image classification,sift
Computer vision,Scale-invariant feature transform,Brain mri,Pattern recognition,Image processing,Appearance based,Feature extraction,Artificial intelligence,Contextual image classification,Medicine,Brain tissue,Magnetic resonance imaging
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Leyla Zhuhadar114617.53
Gopi Chand Nutakki242.55