COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements

Abstract

This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used.

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

“…In particular, regional features were first extracted from brain regions/clusters exhibiting significant group differences examined by two-sample Hotelling's T-square statistic of tissue density and PET scan intensity values; local clusters were formed using a watershed-based region growing technique described in (Fan et al, 2007b). A feature reduction process was then applied, using a feature selection method which identifies a minimal set of brain regions that jointly provide optimal separation between MCI and cognitively normal (CN) (Fan et al, 2007b). This step is very important, as it identifies a minimal set of regions, which will be referred to as optimally differentiating clusters (ODC) in the remainder of the paper, which jointly maximally differentiate between MCI and CN individuals on an individual scan basis.…”

“…Since the main goal of this paper is to test diagnostic tools for individual scans, rather than to identify statistical differences between two potentially overlapping groups, we applied highdimensional pattern classification (Fan et al, 2007b) in addition to voxel-based group analyses. In particular, regional features were first extracted from brain regions/clusters exhibiting significant group differences examined by two-sample Hotelling's T-square statistic of tissue density and PET scan intensity values; local clusters were formed using a watershed-based region growing technique described in (Fan et al, 2007b).…”

“…However, VBM analyses are of limited value for individual diagnosis, since they measure group differences and are not equipped to classify individuals. Towards this goal, high-dimensional pattern classification methods have been pursued in recent years (Davatzikos, 2004;Duchesne et al, 2006;Fan et al, 2006a;Fan et al, 2007a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Lao et al, 2004;Liu et al, 2004;Timoner et al, 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al, in press, 2006;Davatzikos et al, 2005a;Davatzikos et al, 2005b;Fan et al, 2008a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Kawasaki et al, 2007;Mourao-Miranda et al, 2005;Yoon et al, 2007). Unlike VBM-type methods, which are mass univariate and don't consider statistical associations among different brain regions, high-dimensional pattern classification methods are multivariate, thereby leading to better group separation, which is critical for individual diagnosis.…”

“…In the current paper, we investigate the added value of combining structural MRI and PET images of regional cerebral blood flow (rCBF) in discriminating between MCI and normal controls. We apply techniques of adaptive regional feature extraction, feature selection, and multivariable support vector machine classification, which construct a multivariable classifier by learning from training samples (Fan et al, 2005;Fan et al, 2007b). In addition, we extend our approach using a technique called bagging (Breiman, 1996;Duda et al, 2001;Fan et al, 2008b), to obtain more conservative estimates of how well this approach is likely to generalize to new individuals.…”

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI

“…This procedure was repeated for all individuals. A key difference of the classifier used herein from the one used in (Fan, Shen et al 2007) is that in our current study, the SVM kernel size was automatically estimated separately for each individual to be the kernel that yielded the maximum classifier response, i.e. the maximum absolute value of the decision function.…”