Probability-based Scoring for Normality Map in Brain MRI Images from

Normal Control Population

Thach-Thao Duong

LaBRI, University of Bordeaux

351 cours de la Liberation 33400 Talence, France

Keywords:

Alzheimer, Normality Map, Classiﬁcation, Sparse-based.

Abstract:

The increasing availability of MRI brain data opens up a research direction for abnormality detection which

is necessary to on-time detection of impairment and performing early diagnosis. The paper proposes scores

based on z-score transformation and kernel density estimation (KDE) which are respectively Gaussian-based

assumption and nonparametric modeling to detect the abnormality in MRI brain images. The methodolo-

gies are applied on gray-matter-based score of Voxel-base Morphometry (VBM) and sparse-based score of

Sparse-based Morphometry (SBM). The experiments on well-designed normal control (CN) and Alzheimer

disease (AD) subsets extracted from MRI data set of Alzheimer’s Disease Neuroimaging Initiative (ADNI)

are conducted with threshold-based classiﬁcation. The analysis of abnormality percentage of AD and CN

population is carried out to validate the robustness of the proposed scores. The further cross validation on

Linear discriminant analysis (LDA) and Support vector machine (SVM) classiﬁcation between AD and CN

show signiﬁcant accuracy rate, revealing the potential of statistical modeling to measure abnormality from a

population of normal subjects.

1 INTRODUCTION

The abnormality detection for brain imaging has been

emerged as an attractive research ﬁeld in which the

aim is to identify the areas of impairment or abnor-

mality in the brain structure. The automatic mea-

surement of abnormality can be served as a reference

for doctors to accurately diagnose various patholog-

ical diseases. In detection of impairment in brain

structure, the difﬁculty increases from detecting tu-

mors, multiple sclerosis (MS), Alzheimer disease. In

detection of impairment in brain structure, detecting

multiple sclerosis (MS) is more difﬁcult than detect-

ing tumors. Moreover, the abnormality detection of

Alzheimer disease is more difﬁcult than both tumor

and MS because the impairments in Alzheimer ap-

pear at small or tiny areas of the brain. Impairment in

tumor shows in largest areas while multiple sclerosis

appears at several average sized locations in the brain.

Over the recent years, various automatic methods

for abnormality detection have been proposed. While

there are methods to learning the abnormality via nor-

mal and varied impairment subjects, there is still lack

of methods to measure the general abnormality solely

from the normal subjects. Among them, dictionary

learning and sparse coding is currently common in

tackling in multiple sclerosis (MS) (Deshpande et al.,

2015) and brain tumors (Irimia et al., 2012). In an-

other work, the dictionary learn from healthy brain

image tissue and sparse coding are used to automati-

cally segment multiple sclerosis (MS) lesion via un-

supervised method (Weiss et al., 2013). Having the

assumption that image patches of higher reconstruc-

tion errors contain lesions, a thresholding scheme on

the errors is used for segmentation of MS lesions.

As one of the most popular method for auto-

matic analysis of brain structure (Ashburner and Fris-

ton, 2000), VBM has been successfully applied in

the research of disorder (Radua and Mataix-Cols,

2009), aging (Huttona et al., 2009) and gender dif-

ferences (Gooda et al., 2001). While it lacks local

brain anatomy representation, patch-based approach

is efﬁcient to capture local anatomical pattern and

inter-subjects variability. The patch-based methods

have achieved potential results in some applications

of MRI image analysis.

The main goal of this work is to to measure nor-

mality pattern at voxel level from solely normal con-

trol subjects. Recently, there is a similar work propos-

ing z-score transformation to compute abnormality

via population of normal subjects in a case study of

the children brain (Wilke et al., 2014). This work is

based on VBM methodology, where z-score is calcu-

lated from distribution of Gray Matter (GM), White

256

Duong, T-T.

Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population.

DOI: 10.5220/0005724702540261

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 256-263

ISBN: 978-989-758-175-5

Copyright

c

2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Matter (WM) and Cerebrospinal Fluid (CSF). A low-

rank and sparse components is used to exploit popula-

tion information and to identify inconsistent parts of

an image from the population which are more likely

lesions (Liu et al., 2014). However, these methods do

not construct an abnormality score at voxel level.

This work constructs a methodology to investigate

and to analyse the normality score generated from

VBM and patch-based methodology. To validate the

score, the classiﬁcation is conducted on AD and CN

subjects from Alzheimer’s Disease Neuroimaing Ini-

tiative (ADNI) database. The contributions of this

work are : (i) parametric and nonparametric statistical

models at voxel level are presented to score abnormal-

ity from a training population of normal control sub-

jects, (ii) the abnormality scoring is conducted from

VBM and SBM methodologies, and (iii) the scores

are validated by the classiﬁcation of AD and CN sub-

jects from ADNI database. The threshold-based is

used to validate the discrimination between AD and

CN scores while cross validation of LDA and SVM

is conducted to validate the robustness of the scores

with SVM and LDA classiﬁers.

2 SPARSE-BASED

MORPHOMETRY

The Sparse-Based Morphometry (SBM) is the

methodology based on the patch-based representation

(Mairal et al., 2009) at each voxel level. The patch-

based dictionary is constructed and patch-based re-

construction error is calculated from the dictionary.

This paper calls the patch-based reconstruction errors

the sparse code. The dictionary is constructed sep-

arately for each voxel from sparse code of normal

anatomical patterns. At each voxel, after being ex-

tracted and centered by subtraction of its means, a 3D

patch is normalised by dividing its norm and is vec-

torised into a vector x

i

. The dictionary of each voxel v

is constructed as a collection of N patches where N is

the size of dictionary set D . Function (1) determines

the level of sparsity where γ is a positive parameter.

The SPAMS toolbox (Mairal et al., 2009)

1

is used

to solve optimization in Function (1). Each column

of dictionary D is a vectorization of a patch encoding

the possible local morphological conﬁgurations of the

brain in the reference group D. The approximation

Dα

i

is calculated to guarantee its best closeness to the

patch x

i

via the term

1

2

kx

i

− Dα

i

k

2

2

k. The purpose of

the term kα

i

k is to ensure that each extracted patch x

i

is able to be decomposed in a sparse way in the output

1

http://spams-devel.gforge.inria.fr/index.html

dictionary D.

F = min

D,α

i

|D|

∑

i=1

1

2

kx

i

− Dα

i

k

2

2

+ γkα

i

k

1

(1)

At a voxel v of an input subject Q, a patch p

v

Q

is processed identically to the dictionary construc-

tion. Via the reconstruction mapping, the sparse-

based score SM is the reconstruction cost to retrieve

the closed relevant patch matched from the dictionary

at the corresponding location.

SM

v

Q

= f (p

v

Q

, v) = min

α∈R

p

1

2

kp

v

Q

−D

v

αk

2

2

+γkαk

1

(2)

D

v

is the dictionary associated with the voxel at

location v. The dictionary D is expected to have the

capacity to sparsely reconstruct majority of the nor-

mal patches learned in D. Therefore, giving an abnor-

mal brain subject such as AD, an approximation error

1

2

kx − D

v

αk

2

2

would converge to a low sparity level,

resulting statistical high values of kαk

1

and SM

v

Q

. Ac-

cordingly, the value of function f is relatively high for

abnormal brain anatomy voxel and relatively low for

normal voxel.

3 NORMALITY SCORING

This paper employs a subpart of the standardised

ADNI1 collection (Wyman et al., 2013) which is

a commonly used dataset in Alzheimer disease re-

search. Two groups of cognitively normal subjects

(CN), named “CN Dictionary” and “CN Training”,

are constructed for dictionary and for mapping upon

normal population. The CN dictionary is a collection

of sparse representation of normal brain anatomy. The

CN Training is a population representing for normal

brain anatomy on which an unknown-pathology sub-

ject is projected for its normality measurement. In

addition, two groups of CN and AD subjects are ex-

tracted for testing. To minimise bias of the experi-

ments, these four groups are randomly selected with

the same size of 70 subjects and similar correlation in

age and gender distribution.

Figure (1) shows the SBM and VBM methodolo-

gies to generate the abnormality map. The ﬁnal goal

of both methodologies is to measure the abnormality

at voxel level, leading to an abnormality map. SBM

methodology requires a Dictionary of patches con-

structed at each voxel to measure the sparse-based re-

construction cost SM. The SM values of an input sub-

ject are projected on the distribution of training CN

subjects to measure the abnormality at the associated

voxel location. This procedure is repeated with gray

Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population

257

Training subjects

x

Distribution of SM from training CN

Input subject

Dictionary

Abnormality Map

Distribution of GM from training CN

x

Transformation

Sparse-based framework Vorxel-based framework

z-score

KDE

Input subject

Training subjects

R

e

c

o

n

s

t

r

u

c

t

i

o

n

C

o

s

t

Reconstruction

Cost

Figure 1: An overview diagram of the methods.

matter GM value in VBM methodology. The projec-

tion of a single value over a distribution is performed

via z-score and KDE estimation.

3.1 Z-score

The abnormality score is calculated from the projec-

tion upon the training set T . In VBM methodology,

for each voxel, assuming that the distribution of GM

value of T as Gaussian distribution, z-score transfor-

mation is employed to scale it to the standard Gaus-

sian distribution so that the abnormality score is mea-

sured upon the same standard normal distribution. For

the input subject S, the zGM score is computed ac-

cording to the Functions (3) and (5). GM score of the

subjects Q at voxel v is denoted as GM

v

Q

. The zGM

v

S|T

is the GM score at voxel v of the subject Q projected

on the normal population T . The score zGM of the

subjects S is the average of zGM

v

. The µ

v

T

and σ

v

T

are mean and standard deviation of the distribution of

GM values of subjects in T at the particular voxel v,

denoted as {GM

v

|T }.

zGM

v

S|T

=

GM

v

S

− µ

v

T

σ

v

T

(3)

zSM

v

S|T

=

SM

v

S

− µ

v

T

σ

v

T

(4)

zGM

S|T

= avg

v

{zGM

v

S|T

} (5)

zSM

S|T

= avg

v

{zSM

v

S|T

} (6)

Similarly in SBM methodology, the zSM is calcu-

lated with the Functions (4) and (6). µ

v

T

and σ

v

T

are

the mean and the standard deviation of the distribution

{SM

v

|T }. zSM

S|T

denotes the score transformed by

z-score over the distribution of the normal population

T .

3.2 Kernel Density Estimation (KDE)

The normality measurement can be addressed by the

statistical principle of anomaly detection which is

stated that “an abnormality is an observation which is

suspected of being partially or wholly irrelevant be-

cause it is not generated by the stochastic model as-

sumed” (Anscombe and Guttman, 1960). To avoid

biased assumption of the underlying distribution of

the stochastic model, kernel density estimation (KDE)

(Rosenblatt, 1956; Parzen, 1962) which is a nonpara-

metric technique to estimate the density probability

distribution and to measure the probability of x be-

longing to the distribution X. This probability P(x, X )

for x to belong to the distribution X is calculated by

the default KDE function in MATLAB.

Given an input GM

v

S

, the probability of that voxel

to be normal is the probability of that voxel belonging

to the distribution computed according to the Func-

tions (7) and (9). The normality score of the sub-

ject S is calculated as the average of the score kGM

v

.

kGM

S|T

denotes the score transformed by KDE over

the distribution of normal population T .

kGM

v

S|T

= P(GM

v

S

|{GM

v

|T }) (7)

kSM

v

S|T

= P(S M

v

S

|{SM

v

|T }) (8)

kGM

S|T

= avg

v

{kGM

v

S|T

} (9)

kSM

S|T

= avg

v

{kGM

v

S|T

} (10)

Similar to the score kGM, the sparse-based nor-

mality score by KDE is also computed by the Func-

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

258

tions (8) and (10). kGM

S|T

denotes the score trans-

formed by KDE over the distribution of normal pop-

ulation T .

4 EXPERIMENTS

To validate the robustness of proposed scores in mea-

suring the normality at voxel level, the evaluation of

the distinction on Alzheimer patients is performed

four scores and volume calculated within whole brain

and hippocampus since impairment in hippocampus

is the most contributing factor for AD disease (Braak

and Braak, 1991). Because of high complexity in

sparse-based reconstruction from dictionary learning,

the MRI image resolution is scaled down to half of

the original dimension. In addition, in order to reduce

time and resource to process voxels with low conver-

gence of GM, only voxels with the GM higher than

the threshold ε = 0.1 are processed. volume is calcu-

lated as the total number of voxels.

Vol

S

=

∑

v

(GM

v

S

≥ ε) (11)

The Figure (2) provides a comparison between

proposed scores calculated on testing AD and test-

ing CN subjects. The scores kSM, kGM and zGM

measure the normality while the score zSM measure

the abnormality because high SM presents large re-

construction cost from the dictionary and high GM

indicates the high convergence of gray matter. The

ﬁgure reveals that normality score in hippo campus

has wider range than that of the whole brain. More-

over, hippocampus has experienced a more signiﬁcant

difference between AD and CN scores than the whole

brain. This statistically signiﬁcant difference is shown

by the t-test where p ≤ 10

−5

, except the case of kGM

in the whole brain with p = 0.0012.

4.1 Threshold-based Classiﬁcation

The Figure (3) presents the ROC curve of threshold-

based classiﬁcation between AD and CN by volume

and proposed scores. The Table (2) lists the area un-

der the cure (AUC) and the cut-point for each classi-

ﬁcation. It is clearly from the Figure (3) that the clas-

siﬁcation on hippocampus are generally better than

that on whole brain. In addition, the AUC and cut-

point for hippocampus are generally higher than that

of whole brain. In term of AUC, the zSM achieves the

best performance while kSM gains the best accuracy

at cut-point. Seen from the AUC in hippocampus of

the Table (2), the score zSM, kSM and zGM achieve

the comparable AUC with more than 82% and better

than volume. Meanwhile, kGM gains the worse per-

formance among score, which is apparent because its

signiﬁcance p value from t-test are the highest among

others.

The abnormality percentage in the area of obser-

vation (e.g. whole brain or hippocampus) is calculate

to evaluate the abnormality at voxel level. The ab-

normality percentage is the ratio of number of abnor-

mal voxel to the volume of the area of observation.

A voxel is identiﬁed as abnormal if its score is above

the threshold. In the observation of CN population,

the score of every voxel of 70 CN subjects is collected

and ranked. An array of thresholds is calculated based

on training population so that abnormality percentage

of ranging from 0% to 100% of abnormality of the

test CN population T . Accordingly to the array of

thresholds, the abnormality percentage are calculated

for each subjects of the AD and CN population. Plot-

ted in ﬁgure 4, abnormality percentage of AD and CN

population are the averages of abnormality percentage

over subjects within each populations.

The Figure 4 shows the correlation of abnormal-

ity percentage of AD and CN computed whole brain

and hippocampus according to the array of thresh-

olds. The y-axis presents the abnormality percentage

of AD subjects while the x-axis presents the associ-

ated abnormality percentage of CN population from

the identical thresholds. It is clear that the abnor-

mality percentage computed in whole brain is signiﬁ-

cantly higher than that in hippocampus, which means

there are larger areas of abnormality in hippocampus

or abnormal regions in hippocampus. It is clearly

from the Fig. 4 that the kSM and zSM show better

abnormality percentage than kGM and zGM respec-

tively. In a closer look, kSM and zSM show higher

abnormality percentage than kGM and zGM, which

means the kSM and zSM show the robustness of ab-

normality measurement in AD. The kSM and zSM

show the clear distinction in terms of abnormality per-

centage among AD than kGM and zGM.

4.2 Cross-validation with LDA and

SVM

Additional validation the robustness of scores mea-

suring the normality at voxel level are conducted on

classiﬁcation between AD and CN with two clas-

siﬁers Linear discriminant analysis (LDA) (Fisher,

1936) and Support vector machine (SVM) (Cortes

and Vapnik, 95) via leave-one-out-cross-validation

(LOOCV) (Devijver and Kittler, 1982). The LOOCV

is employed to minimise bias.

The Table 2 presents classiﬁcation rates in per-

centage of two classiﬁers LDA and SVM between AD

Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population

259

AD CN AD-H CN-H

Normality

0

10

20

30

40

50

kSM

AD CN AD-H CN-H

Abnormality

0

2

4

6

8

10

12

zSM

AD CN AD-H CN-H

Normality

1.5

2

2.5

3

kGM

AD CN AD-H CN-H

Normality

-2

-1.5

-1

-0.5

0

0.5

1

zGM

Figure 2: The comparison of the (ab)normality score between AD and CN in whole brain and hippocampus region. ’AD-H’

and ’CN-H’ denote the scores calculated in hippocampus.

False positive rate

0 0.2 0.4 0.6 0.8 1

True positive rate

0

0.2

0.4

0.6

0.8

1

ROC for Classification on Hippocampus

Volume zSM kSM zGM kGM

False positive rate

0 0.2 0.4 0.6 0.8 1

True positive rate

0

0.2

0.4

0.6

0.8

1

ROC for Classification on whole brain

Figure 3: ROC curves of proposed scores for threshold-based classiﬁcation between AD and CN.

Table 1: AUC and cut-point in percentage of ROC curves from Figure 3.

Area of Observation Vol zSM kSM zGM kGM

AUC whole brain 79.04 83.39 81.29 76.82 65.90

hippocampus 81.01 86.29 82.22 84.67 73.08

cut-point whole brain 77.14 74.29 74.29 71.43 60.00

hippocampus 74.29 78.57 80.00 78.57 65.71

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

260

0 20 40 60 80 100

Abnormalilty Percentage of CN

0

0.2

0.4

0.6

0.8

1

Abnormalilty Percentage of AD

Whole brain

kSM

zSM

kGM

zGM

0 20 40 60 80 100

Abnormalilty Percentage of CN

0

0.2

0.4

0.6

0.8

1

Hippocampus

kSM

zSM

kGM

zGM

Figure 4: Correlation of Abnormality Percentage in whole brain and hippocampus between AD and CN.

and CN subjects based on the volume, four abnormal-

ity scores transformed from SBM and V BM method-

ology. The classiﬁcation is performed on whole

brain and hippocampus areas. The hippocampus ob-

served in this work comprises four regions of inter-

est (ROI): hippocampus left, hippocampus right, para

hippocampus left, para hippocampus right, which is

deﬁned in the Automatic Anatomical Labeling (AAL)

(Tzourio-Mazoyer et al., 2002). The classiﬁcation

rates are measured in terms of accuracy, sensitivity,

speciﬁcity which are abbreviated as “Acc”, “Sen” and

“Spec” in the tables. Features for classiﬁers LDA and

SVM are “Vol” standing for Volume, zGM, kGM,

zSM and kSM. For convenient presentation, the ex-

pression of “classiﬁer”-“feature” is used to mention

that classiﬁer with the speciﬁc feature. For example,

LDA-zGM represents classiﬁer LDA with zGM.

Seen from the table, while impairment in hip-

pocampus region is the most contributed factors for

AD disease (Braak and Braak, 1991), the volume of

hippocampus gained less improved than the volume

of the whole brain. This fact proved that the fea-

ture on volume does not reveal accurately the abnor-

mality degree of the brain. On the other hand, vol-

ume feature lacks of capability to rank or measure

the abnormality in voxel level instead the score show

the score for brain or region levels. In regard to ac-

curacy rate in whole brain, LDA-Vol, SVM-Vol and

LDA-kSM achieved the best performance at 72.86%.

Most of classiﬁcations with four scores do not im-

prove upon this classiﬁcation rate but classiﬁcation

procedures on hippocampus areas. Among them the

highest accuracy of classiﬁcation is SVM and LDA

with kSM score on hippocampus regions, accounting

for 82.14% and 80.71% respectively. In most of the

case except the case of SVM on zGM score, the score

calculated in hippocampus areas achieved better per-

formance in term of classiﬁcation rate than that calcu-

lated in the whole brain. The improvement of classi-

ﬁcation rate in hippocampus areas reveals the robust-

ness of model in measuring the abnormality score. It

is evident from the table, the z-score transformations

of GM and SM do not signiﬁcantly improve the clas-

siﬁcation rate compared with volume feature both on

whole brain and hippocampus except LDA-zGM with

76.43% and SVM-zGM with 75.71%. In further anal-

ysis, to compute the robustness of the score in terms

of the classiﬁcation rate between the whole brain and

hippo campus areas, the gap between whole brain and

hippocampus is calculated.

The table 3 presents the subtraction classiﬁca-

tion rate of whole brain from hippocampus. This

subtraction is computed on accuracy, sensitivity and

speciﬁcity rate. The feature volume and SVM show

the degraded performance in terms of accuracy rate,

revealing that volume feature is not a proper fea-

ture measuring abnormality. While the SVM-kSM

achieved the best improvement in hippocampus ar-

eas(i.e. 12.14%), the one on zSM degraded perfor-

mance at a negative gap of -2.86%, proving a conclu-

sion that KDE transformation is better than z-score

transformation in computing abnormality score. In

addition, KDE is considerably better than z-score

transformation in two cases of LDA on SM and SVM

on GM (i.e. 7.86% vs 1.43 % and 10.71% vs 4.29%)

while there is a mere degradation of KDE with z-score

on LDA on GM (i.e. LDA-kGM of 5.00% vs LDA-

zGM of 5.71%).

Via KDE transformation, SM score show im-

provements over GM score for particular cases such

as with SVM classiﬁer (i.e. SVM-kSM of 12.14%

vs SVM-kGM of 10.71%), LDA classiﬁer (i.e. LDA-

kSM of 7.86% vs LDA-kGM of 5.00%)

Assuming in the experiments that positive and

Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population

261

Table 2: LDA and SVM classiﬁcation with score of Volume, zGM , kGM, zSM and kSM for whole brain and Hippocampus

region.

LDA SVM

Score whole brain Hippocampus whole brain Hippocampus

Acc (%) Sen (%) Spec (%) Acc (%) Sen (%) Spec (%) Acc (%) Sen (%) Spec (%) Acc (%) Sen (%) Spec (%)

Vol 72.86 78.57 67.14 70.71 80.00 61.43 72.86 78.57 67.14 66.43 82.86 50.00

zGM 70.71 68.57 72.86 76.43 75.71 77.14 71.43 65.71 77.14 75.71 75.71 75.71

kGM 60.71 65.71 55.71 65.71 77.14 54.29 53.57 71.43 35.71 64.29 90.00 38.57

SM 72.14 77.14 67.14 73.57 91.43 55.71 70.71 77.14 64.29 67.86 94.29 41.43

kSM 72.86 81.43 64.29 80.71 84.29 77.14 70.00 81.43 58.57 82.14 87.14 77.14

Table 3: Gap of classiﬁcation rate between whole-brain and

hippocampus.

Score LDA SVM

Acc (%) Sen (%) Spec (%) Acc (%) Sen (%) Spec (%)

Vol -2.14 1.43 -5.71 -6.43 4.29 -17.14

zGM 5.71 7.14 4.29 4.29 10.00 -1.43

kGM 5.00 11.43 -1.43 10.71 18.57 2.86

zSM 1.43 14.29 -11.43 -2.86 17.14 -22.86

kSM 7.86 2.86 12.86 12.14 5.71 18.57

negative are denoted as CN subject and AD subjects,

it is preferable for classiﬁcation methodologies with

high Speciﬁcity because of preventive cure for the dis-

ease. It is clearly from the Table 2, SVM-kSM per-

formed the best in terms of speciﬁcity, accounting for

77.14% while SVM-zGM gained the highest sensitiv-

ity rate at 94.29%. Moreover, in the Table 3, the SVM

and LDA on kSM have signiﬁcant gap of speciﬁcity

rate with 18.57% and 12.86% respectively. On the

other hand, volume feature degraded the speciﬁcity

with negative gaps of -17.14% and -5.71%. There is

a drop in speciﬁcity of 22.86% and 11.43% for SVM

and LDA on zSM respectively. In the case of GM

score, the gaps of speciﬁcity are considerably small

except LDA-zGM at 4.29 %.

5 CONCLUSIONS

This paper has introduced methodologies for abnor-

mality scoring from solely projecting on normal pop-

ulation via VBM and SBM methodologies. Inspired

by the statistical anomaly detection techniques, z-

score transformation, i.e. standardised Gaussian-

based assumption, and KDE, i.e. nonparametric sta-

tistical model, are used. Since the scores are mea-

sured by only CN subjects, the methods can be efﬁ-

ciently implemented without prior knowledge of ab-

normality. The method is applied on GM from VBM

methodology and sparse code from SBM methodol-

ogy. The sparse code is deduced from dictionary

learning and sparse representation of a separate nor-

mal population. Subsets of CN and AD popula-

tion are extracted from ADNI database with similar

cohesion of age and gender. Finally, the proposed

score is evaluated via threshold-based classiﬁcation

and the robustness is demonstrated by achieving the

best AUC and cut-point rates of 86.29% and 80%, re-

spectively. Additionally, AD population has higher

abnormality percentage than CN, demonstrating the

novelty of proposed scores in measurement of nor-

mality. The results are signiﬁcantly potential for fur-

ther applications to the detection of other patholo-

gies since the normality scores calculated solely from

CN patient are able to detect Alzheimer disease with

simple threshold classiﬁcation. Additionally, the pro-

posed score is evaluated via SVM and LDA classi-

ﬁers within LOOCV procedure and the robustness is

demonstrated by gaining the accuracy and sensitivity

rates of more than 80% and 90%, respectively.

ACKNOWLEDGEMENTS

This study has been performed with ﬁnancial support

from the French National Research Agency (ANR) in

the Investments for the future Programme IdEx Bor-

deaux, Cluster of excellence CPU. The dataset was

obtained from the Alzheimer’s Disease Neuroimag-

ing Initiative (ADNI). The author thanks Dr. Pierrick

Coup

´

e for sharing some parts of the dataset for this

study. Dataset providers, however, did not participate

in analysis or writing of this report.

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