Abstract
A project is described with the aim to develop a Computer-Aided Retrieval and Diagnosis of Alzheimer’s disease. The domain of focus is Alzheimer’s disease A manually curated MRI data set from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project ( http://www.loni.ucla.edu/ADNI/ ) was used for training and validation. The system’s main function is to generate accurate matches for any given visual or textual query. The system gives an option to perform the matching based on a variety of feature-sets, extracted using an adaptation of a discrete cosine transform algorithm. Classification is conducted using Support Vector Machines. Finally, ranking of most accurate matches are generated by applying an Euclidean distance score. The overall system architecture follows a multi-level model, permitting performance analysis of components independently. Experimental results demonstrate that the system can produce effective results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akselrod-Ballin, A., Galun, M., Gomori, M., Basri, R., Brandt, A.: Atlas guided identification of brain structures by combining 3D segmentation and SVM classification. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 209–216. Springer, Heidelberg (2006)
Jack Jr., C., Bentley, M., Twomey, C., Zinsmeister, A.: MR imaging-based volume measurements of the hippocampal-formation and anterior temporal-lobe-validation studies. Radiology 176, 205–209 (1990)
Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Shyu, C.R., Marchiori, A.: Automated Storage and Retrieval of Thin-section CT Images to Assist Diagnosis: System Description and Preliminary Assessment. Radiology 228(1), 265–270 (2003)
Mostafa, J., Mukhopadhyay, S., Palakal, M., Lam, W.: A multilevel approach to intelligent information filtering: model, system, and evaluation. ACM Trans. Inf. Syst. 15(4), 368–399 (1997)
Kelemen, A., Szekely, G., Gerig, G.: Elastic model-based segmentation of 3-D neuroradiological data sets. Med. Img. 18(10), 828–839 (1999)
Huang, Q., Dony, R.: Neural network texture segmentation in equine leg ultrasound images. In: Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1269–1272 (2004)
Sorwar, G., Abraham, A., Dooley, L.: Texture Classification Based on DCT and Soft Computing. In: FUZZ-IEEE, pp. 545–548 (2001)
Ngo, C.W., Pong, T., Chin, R.T.: Exploiting Image Indexing Techniques in DCT Domain. In: IAPR International Workshop on Multimedia Media Information Analysis and Retrieval, pp. 1841–1851 (1998)
Singh, P.K.: Unsupervised segmentation of medical images using dct coefficients. In: VIP 2005: Proceedings of the Pan-Sydney area workshop on Visual information processing, pp. 75–81. Australian Computer Society, Inc., Australia (2004)
Li, J., Allinson, N., Tao, D., Li, X.: Multitraining Support Vector Machine for Image Retrieval. IEEE Transactions on Image Processing 15(11), 3597–3601 (2006)
Pontil, M., Verri, A.: Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 637–646 (1998)
Wang, C.M., Mai, X.X., Lin, G.C., Kuo, C.T.: Classification for Breast MRI Using Support Vector Machine. In: CITWORKSHOPS 2008: Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops, Washington, DC, USA, pp. 362–367. IEEE Computer Society, Los Alamitos (2008)
Du, X., Li, Y., Yao, D.: A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation. In: ICNC 2008: Proceedings of the 2008 Fourth International Conference on Natural Computation, Washington, DC, USA, pp. 49–53. IEEE Computer Society, Los Alamitos (2008)
Mourao-Miranda, J., Bokde, A.L., Born, C., Hampel, H., Stetter, M.: Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. NeuroImage 28(4), 980–995 (2005); Special Section: Social Cognitive Neuroscience
Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.J.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Agarwal, M., Mostafa, J. (2010). Image Retrieval for Alzheimer’s Disease Detection. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2009. Lecture Notes in Computer Science, vol 5853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11769-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-11769-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11768-8
Online ISBN: 978-3-642-11769-5
eBook Packages: Computer ScienceComputer Science (R0)