SPECT Image Classification Techniques for Computer Aided Diagnosis of the Alzheimer Disease

Authors:
J. Ramírez;R. Chaves;J. M. Górriz;M. López;D. Salas-Gonzalez;I. Álvarez;F. Segovia
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

Citing 4
Cited 2

An Automatic Threshold-Based Scaling Method for Enhancing the Usefulness of Tc-HMPAO SPECT in the Diagnosis of Alzheimer's Disease

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Effective Emission Tomography Image Reconstruction Algorithms for SPECT Data

ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Early Detection of the Alzheimer Disease Combining Feature Selection and Kernel Machines

Advances in Neuro-Information Processing

Exploratory matrix factorization for PET image analysis

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of AD patients has increased, early diagnosis has received more attention for both social and medical reasons. Currently, accuracy in the early AD diagnosis is below 70% so that AD does not receive a suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper evaluates different pattern classifiers for the development of a computer aided diagnosis (CAD) system for improving the early AD detection. Discriminant template-based normalized mean square error (NMSE) features of several coronal slices of interest (SOI) were used. The proposed system yielding a 97% AD diagnosis accuracy, reports clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.