PET Image Classification Using HHT-Based Features Through Fractal Sampling
Andrés Ortiz,Francisco Lozano,Alberto Peinado,M. J. Garía-Tarifa,Juan Manuel Górriz,Javier Ramírez +5 more
- 19 Jun 2017
- pp 314-323
TL;DR: A technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EMD) method that has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) achieving up to a 92% accuracy.
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Abstract: Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is specially important in the early diagnosis of dementias In this work we present a technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EMD) method This requires to transform the 3D PET image into a time series which is addressed by sampling the image using a fractal-based method which allows to preserve the spatial relationship among voxels The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) achieving up to a 92% accuracy in a differential diagnosis task (AD vs controls), which proves that the information retrieved by our methodology is significantly related to the disease
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Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.
Andrés Ortiz,Jorge Munilla,Francisco Jesús Martínez-Murcia,Juan Manuel Górriz,Javier Ramírez +4 more
TL;DR: A technique that allows using specific time series analysis techniques with 3D images using a fractal-based method to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis.
References
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Eigenfaces for Recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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