LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease

Intelligent Database Systems Lab

Presenter : CHANG, SHIH-JIE

Authors : Andrés Ortiz , Juan M. Górriz , Javier Ramírez ,

F.J. Martínez-Murcia

2013.PRL

LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease


Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

The Alzheimer’s disease is at an advanced stage and there is no a known cure for the AD disease since currently .


Objectives

• In order to deal with objective diagnosis of the AD, this paper use many techniques to diagnosis more effective better than before.


Methodology- ADNI DB(25Normal、 25AD)


Methodology -Segmentation

Feature extraction

CONN linkage for SOM clustering

segmentation process : two stages 1. Classification 2. SOM Clustering


Methodology


Methodology

Use LVQ3 algorithm:

Length w=


Methodology

feature reduction :

Feature generation, computed reduced features


Methodology


Methodology - SVM

Function h:

Radial Basis Function


Experiments 檢測為正確檢測為錯誤

Disease (+) 生病

number of True Positives

(TP)

number of False Negatives

(FN)

Disease (-) 健康

number of False Positives

(FP)

number of True Negatives

(TN)


Experiments – Classification results


Experiments


Experiments


Conclusions– The results provided by the presented method

outperform other previous approaches based on MRI images. .


Comments• Advantages

– Good classification• Applications

– Diagnosis Alzheimer’s disease

Documents

LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease