International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.8, No.6, Jun. 2016
Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region
Full Text (PDF, 526KB), PP.13-20
The aim of this research is to propose a methodology to classify the subjects into Alzheimer disease and normal control on the basis of visual features from hippocampus region. All three dimensional MRI images were spatially normalized to the MNI/ICBM atlas space. Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation. Texture features were represented on slice by slice basis by mean and standard deviation of magnitude of Gabor response. Classification between Alzheimer disease and normal control was performed with linear support vector machine. This study analyzes the performance of Gabor texture feature along each projection (axial, coronal and sagittal) separately as well as combination of all projections. The experimental results from both single projection (axial) as well as combination of all projections (axial, coronal and sagittal), demonstrated better classification performance over other existing method. Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease.
Cite This Paper
Prateek Keserwani, V. S. Chandrasekhar Pammi, Om Prakash, Ashish Khare, Moongu Jeon,"Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.6, pp.13-20, 2016.DOI: 10.5815/ijigsp.2016.06.02
A. Kumar, J. Kim, W. Cai, M. Fulham and D. Feng, "Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data," Journal of Digital Imaging, vol. 26, pp. 1025-1039, 2013.
Alzheimer disease fact sheet, Alzheimer disease education and retrieval center, National Institute of health, NIH publication number: 11-6423, 2012.
Alzheimer's Disease Facts and Figure, Alzheimer's Association, vol. 10, 2014.
G. B. Frisoni, N. C. Fox, C. R. Jack, P. Scheltens and P. M. Thompson, "The clinical use of structural MRI in Alzheimer disease," Nature Reviews Neural, vol. 6, pp. 67-77, 2010.
N. Villain, B. Desgranges, F. Viader, V. De La Sayette, F. Mézenge, B. Landeau, J. C. Baron, F. Eustache and G. Chételat, "Relationships between Hippocampal Atrophy, White Matter Disruption, and Gray Matter Hypometabolism in Alzheimer's disease," The Journal of Neuroscience, vol. 28, pp. 6174-6181, 2008.
C. B. Akgül, D. L. Rubin, S. Napel, C. F. Beaulieu, H. Greenspan and B. Acar, "Content-Based Image Retrieval in Radiology: Current Status and Future Directions," Journal of Digital Imaging, vol. 24, pp. 208-222, 2011.
F. Bianconi and A. A. Fernández, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, pp. 3325-3335, 2007.
M. Agarwal and J. Mostafa, "Image Retrieval for Alzheimer's Disease Detection," Medical Content-Based Retrieval for Clinical Decision Support, vol. 5853, pp. 49-60, 2010.
M. Agarwal and J. Mostafa, "Content-Based Image Retrieval for Alzheimer's Disease Detection," Content-Based Multimedia Indexing (CBMI), pp. 13-18, 2011.
M. Mizotin, J. Benois-Pineau, M. Allard and G. Catheline, Feature-based brain MRI retrieval for Alzheimer disease diagnosis, ICIP, pp. 1241-1244, 2012.
O. Ben Ahmed, J. Benois-Pineau, C. B. Amar, M. Allard and G. Catheline, "Early Alzheimer disease detection with bag-of-visual-words and hybrid fusion on structural brain MRI," Content-Based Multimedia Indexing (CBMI), pp. 79-83, 2013.
O. Ben Ahmed, J. Benois-Pineau, M. Allard, C. B. Amar and G. Catheline, "Classification of Alzheimer's disease subjects from MRI using hippocampal visual features," Multimedia Tools and Applications, vol. 74, pp. 1249-1266, 2015.
R. Roslan and N. Jamil, "Texture Feature Extraction using 2-D Gabor Filters," ISCAIE, pp. 173-178, 2012.
U. Bagci and L. Bai, "Detecting Alzheimer Disease in Magnetic Resonance Brain images Using Gabor Wavelets," 15th IEEE conference on Signal Processing and Communication Applications, pp. 1-4, 2007.
P. Padilla, J. M. Górriz, J. Ramirez, R. Chaves, F. Segovia, I. Alvarez, D. Salas-González, M. López and C. G. Putonet, "Alzheimer's disease detection in functional images using 2D Gabor wavelet analysis," Electronics letters, vol. 46, pp. 556-558, 2010.
J. Llonen, J. K. Kamarainen and H. Kalviainen, "Efficient computation of Gabor features," Research Report 100, Lappeenranta University of Technology, Department of Information Technology, 2005.
N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, E. Mazoyer and M. Joliot, "Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single Subject Brain," NeuroImage, vol. 15, pp. 273-289, 2002.
C. M. Florkowski, "Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curves and Likelihood Ratios: Communicating the Performance of Diagnostic Tests," The Clinical Biochemist Reviews, vol. 29, pp. 583-587, 2008.
D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris and R. L. Buckner, "Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults," Journal of Cognitive Neuroscience, 19, pp. 1498-1507, 2007.
R. Manni, Y. H. Yang and S. Kalra, Voxel based texture analysis of the brain, PloS one, 2015, DOI: 10.1371.
A. Khare, M. Khare and R. K. Srivastava, Dual tree complex wavelet transform based multiclass object classification, 12th International Conference on Machine Learning and Applications (ICMLA), pp. 5010-506, 2013.