An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Image interpolation and resampling
Handbook of medical imaging
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Near optimum estimation of local fractal dimension for image segmentation
Pattern Recognition Letters
Multi-Modality Image Registration Maximization of Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
An interactive interface for seizure focus localization using SPECT image analysis
Computers in Biology and Medicine
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Texture analysis of poly-adenylated mRNA staining following global brain ischemia and reperfusion
Computer Methods and Programs in Biomedicine
Texture analysis by multi-resolution fractal descriptors
Expert Systems with Applications: An International Journal
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Single photon emission computed tomography (SPECT) is an accurate imaging method for the diagnosis of refractory partial epilepsy. Two scans are carried out: interictal and ictal. The interest of this method is to provide an image in the ictal period, which allows hyperperfused areas linked to the seizure to be localized. The epileptic foci localization is improved by subtracting the two acquisitions (subtracted ictal SPECT: SIS). In some cases, the SIS method is not effective and does not isolate the seizure foci. In this article, we investigate a new method based on texture analysis using fractal geometry features. Fractal geometry features were extracted from each scan in order to quantify the heterogeneity change resulting from the hyperperfusion. A support vector machine (SVM) classification algorithm was used to classify the voxels into two classes: focal and healthy. Quantitative evaluation was performed on simulated images and clinical images from 22 patients with temporal lobe epilepsy. Results on both experiments showed that the proposed method is more specific and more sensitive than the SIS method.