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1Alzheimer’s Disease | www.smgebooks.comCopyright Dessouky MM.This book chapter is open access distributed under the Creative Commons Attribution 4.0 International License, which allows users to download, copy and build upon published articles even for com-mercial purposes, as long as the author and publisher are properly credited.

Gr upSMFeature Extraction of AD using Different

Proposed Algorithms

Mohamed M Dessouky*, Mohamed A Elrashidy, Taha E Taha and Hatem M AbdelkaderDepartment of Computer Science and Engineering, University of Menoufiya, Egypt

*Corresponding author: Mohamed M Dessouky, Department of Computer Science and Engineering, Faculty of Electronic Engineering, University of Menoufiya, Egypt, Tel: +2 0100-0580-440; Email: [email protected]

Published Date: Feb 10, 2017

Digital image processing is defined as the science of modifying digital images by means of a digital computer. There is almost no area that is not impacted in some way by digital image processing such as remote sensing, image transmission, medical processing, radar, sonar, robot and machine vision. Medical Image Processing (MIP) has been undergoing a revolution in the past decade with the advent of faster, more accurate, and less invasive devices. Pattern recognition and feature extraction of medical imaging is one of the important fields of digital image processing which will be concentrated in this chapter.

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the unknown patterns. Feature extraction is one of an important steps in pattern recognition system. Feature extraction aims to locate significant feature regions on images depending on their intrinsic characteristics and applications. These regions can be defined in global or local neighborhood and distinguished by shapes, textures, sizes, intensities, statistical properties, and so on.


This chapter presents the extraction the most significant features from Alzheimer’s disease (AD) 3D Magnetic Resonance Images (MRI) to help the physicians to make the diagnosis of the AD is more easy and fast especially in the first stages. First, this chapter gives a small introduction about the AD. Then, different proposed algorithms are described with results, comparison study among these different proposed algorithms. Finally, an application had been designed to help the doctors to fast and easy diagnose the AD.

INTRODUCTION TO ALZHEIMER’S DISEASEAlzheimer’s disease (AD) is the most common form of dementia. AD is a degenerative brain

disease which caused because of the death of brain cells causes memory loss and cognitive decline and causes the total brain size to shrink and the tissue has progressively fewer nerve cells and connections. Figure 1 presents different main areas of the brain have one or more specific functions. There is no known cure for AD regardless of when it is detected. Some drugs are available which may help slow the worsening of AD symptoms for a limited time. The early detection of AD will be key to prevent, slow and stop the AD. The last 10 years have seen a tremendous growth in research on early detection [1,2].

Figure 1: The major areas of the brain have one or more specific functions [1,2].

AD is becoming a more common cause of death. Although other major causes of deaths had been decreased significantly, the deaths from AD had been increased significantly according to the indication comes from the official records as shown in Figure 2.


Figure 2: Percentage changes in selected causes of death (all ages) between 2000 and 2013 [2].

AD has several factors that affects its development. These factors known as risk factors such as: age, apolipoprotein E (APOE)-e4 Gene, family history, Mild Cognitive Impairment (MCI), education, social and cognitive engagement, and Traumatic Brain Injury (TBI) [2].

The Signs and Symptoms of the AD

AD is a progressive disease, which means that it worsens over time. Its symptoms can be diagnosed at any stage and after the initial diagnosis, the progression through the stages of the disease can be also monitored when the evolving symptoms transcribe how care is managed. The most five symptom areas of AD are listed below [3]:

1. Worsened ability to take in and remember new information,

2. Impairments to reasoning, complex tasking, exercising judgment

3. Impaired visuospatial abilities

4. Impaired speaking, reading and writing

5. Changes in personality and behavior

Tests and Diagnosis

The diagnosis of AD is very difficult due to the lake of single test for it, the lake of specific blood or imaging test, and lake of biological test. The diagnosis of AD is mainly depends on elimination process. So, the first thing doctors do is ruling out other dementia before considering and confirming that the cause is AD. Next, the doctors review medical history, evaluate mood and mental status, do some physical exam, diagnostic tests and neurological exam [3,4].


Stages of Alzheimer’s Disease

The progression of AD could be divided into three main and important stages which are [1, 2]:

o Preclinical ( there no signs or symptoms appeared on the patient)

o Mild cognitive impairment

o Dementia

These three stages had been broken by the Alzheimer’s Association into seven stages from normal stage to very severe stage. The stages below provide a general idea of how abilities change during the course of the disease [5]:

Stage 1: No impairment

Stage 2: Very mild decline

Stage 3: Mild decline

Stage 4: Moderate decline

Stage 5: Moderately severe decline

Stage 6: Severe decline

Stage 7: Very severe decline

NEUROIMAGING MATERIALS AND DATABASEThe dataset that has been used for this chapter is the Open Access Series of Imaging Studies

(OASIS) database. OASIS database used to determine the sequence of events that happened in the progression of AD. The database consists of a cross-sectional collection of 416 subjects aged between 33 to 96 years. The subjects include normal subjects that has no disease (no dementia) with Clinical Dementia Rating (CDR) of 0, subjects have been diagnosed with very mild AD (CDR=0.5), subjects are diagnosed with mild AD (CDR=1) and subjects with moderate AD (CDR=2) [6,7].

The subjects include both men and women. More women than men have AD and other dementias because of the fact that women live longer than men on average and older age is the greatest risk factor for AD. Almost two-thirds of AD are women. It is also found that there is no significant difference between men and women in the proportion who develop AD or other dementias at any given age [2].


Figure 3 illustrates two subjects, the first one is normal subject as shown in Figure 3 (a). The second subject is for AD patients diagnosed with “mild AD” is depicted in Figure 3 (b). Each subject is represented by 3 planes or views (X-Y, Y-Z, and X-Z planes).

(a) Non-demented (normal) subject (b) Demented (patient) subject

Figure 3: The three planes of non-demented and demented images [6,7].

All participants underwent 3-D and MRI scans. The subjects were randomly chosen to cover an old ages, with different ranges of MMSE. The female subject is more than the male because of that the female is more susceptible to AD than males because the females are older age as denoted before. Table 1 summarizes important clinical and demographic information for each group [6,7].

Table 1: Summary of subject demographics and dementia status.

Group AD (Very Mild – Mild) Normal

Age 62-90 33-94

Clinical Dementia Rating (CDR) 0.5–1 0

Mini-Mental State Examination (MMSE) 15-30 25-30

Education 1-5 1-5

Socioeconomic Status (SES) 1-5 1-5

THE DIFFICULTY OF THE ALZHEIMER’S DISEASE PROBLEMIn this section, various and important statistical, structural, and textural features of MRI brain

images had been extracted and analyzed [8]. These statistical features had been used for detection of the abnormalities among different demented and non-demented MRI AD images. There are different previous algorithms that depends on extracting statistical, textural, and structural features from digital images in different application [9,10].


Statistical Feature

Statistical features are defined as the distribution of the gray level in the pixels of an image. The gray scale is a black (0) and white (255) image at any given focus of pixel. That means an image is composed of an array of pixels of varying intensity across the image, the intensity corresponding to the level of grayness from black to white at any point in the image. There are different first order statistical features had been extracted like: Pixel Count, Energy, Entropy, Mean Absolute Deviation (MAD), Root Mean Square (RMS), Standard Deviation, Skewness, Kurtosis, Variance, and Uniformity [11,12].

Shape, Geometric and Structural Features

These features describe the shape and size of the ROI. Let (V) be the volume and (A) the surface area of the ROI. There are different features in the shape and geometric features but only the compacteness1 had been measured in this work: Compactness1 [11,12].

Textural Features

Textural features describes patterns or the spatial distribution of voxel intensities, which were calculated from respectively gray level co-occurrence (GLCM) and gray level run-length (GLRLM) texture matrices. Determining texture matrix representations requires the voxel intensity values within the VOI to be discretized.

There are different GLCM based features in the textural features had been extracted and measured in this work like: Autocorrelation, Cluster Prominence, Cluster Shade, Cluster Tendency, Dissimilarity, Homogeneity2, and Inverse Different Moment Normalized (IDMN) [11,12].

There are different Gray-Level Run-Length matrix based features in the textural features had been extracted and measured in this work like: Short Run Emphasis (SRE), Long Run Emphasis (LRE), Gray Level Non-Uniformity (GLN), Run Length Non-Uniformity (RLN), Run Percentage (RP), Low Gray Level Run Emphasis (LGLRE), High Gray Level Run Emphasis (HGLRE), Short Run Low Gray Level Emphasis (SRLGLE), Short Run High Gray Level Emphasis (SRHGLE), Long Run Low Gray Level Emphasis (LRLGLE), and Long Run High Gray Level Emphasis (LRHGLE) [11,12].

All these different features had been extracted from different AD MRI images in three stages. These stages are:

a) Stage 1: No Impairment (normal subject)

b) Stage 2: Very mild cognitive decline

c) Stage 3: Mild cognitive decline

The statistical features had been measured for 3 images, each image represents one stage. Table 2 summarize the obtained results of the statistical features associated with the X-Y plane of the three images.


Table 2: The statistical features for the X-Y plane of the three images in the three stages.

Stage features

Stage 1Normal image

Stage 2Very Mild AD

Stage 3Mild AD

X-Y Plane

Pixel Count 18499 18742 18564

Energy 17420669043 18725791485 15889204627

Entropy 69676.23 71734.9 70030.08

Mean Deviation 364.91 352.87 399.24

Root mean square 970.42 999.57 925.16

Standard Deviation 422.01 410.9 452.72

Skewness -0.12 -0.31 0.17

Kurtosis -1.22 -1.06 -1.26

Variance 178096.6 168842.9 204956.54

Uniformity 272709 295568 308276

Compactness 1 9.29 9.31 9.3

Auto-correlation 4767759033.54 5528963513.54 4419420592.92

Cluster Prominence 1.03e+17 1.25e+17 1.01e+17

Cluster Shade 4.06e+13 5.09e+13 4.27e+13

Cluster Tendency 18777303760 21772844218.4 17454124968.5

Dissimilarity 624751.5 620013.8 655053.69

Homogeneity 2 78.55 76.4 79.9

IDMN (Inverse Difference Moment) 5628.69 5690 5647.62

SRE 0.304 0.3041 0.3039

LRE 1.05 1.08 1.09

GLN 4.25 4.52 4.55

RLN 5254.53 5325.23 5249.68

RP 0.292 0.292 0.291

LGLRE 3.25e-05 3.05e-05 3.33e-05

HGLRE 260828.2 297312.6 247112.51

SRLGLE 2.84e-05 2.75e-05 2.06e-05

SRHGLE 259388.1 296018 246317.74

LRLGLE 0.0009 0.0008 0.0023

LRHGLE 333478 345511.5 294586.73


Table 3: The statistical features for the X-Z plane of the three images in the three stages.

Stage features

Stage 1Normal image

Stage 2Very Mild AD

Stage 3Mild AD

X-Z Plane

Pixel Count 16656 16435 16415

Energy 8752863721 9061132322 9900535633

Entropy 64888.4 66430.36 62694.37

Mean Deviation 252.92 218.01 266.96

Root mean square 724.92 742.52 776.62


Skewness 0.84 0.93 0.67

Kurtosis 0.454 0.96 -0.06

Variance 105013.8 79762.9 109971.5

Uniformity 296934 329571 266421

Compactness 1 8.943 8.91 8.91




Cluster Tendency 10118127383.1 7566447191.7 8640481294.9

Dissimilarity 480067.9 446495.2 483583.1

Homogeneity 2 83.17 78.94 81.21

IDMN (Inverse Difference Moment) 5053.85 4991.46 4985.385

SRE 0.3031 0.303 0.303

LRE 2.92 3.08 3.11

GLN 5.04 5.59 4.63

RLN 4644.91 4548.23 4593.27

RP 0.288 0.286 0.29

LGLRE 3.44e-05 3.58e-05 3.61e-05

HGLRE 160713.4 122824.8 139816.4

SRLGLE 3.16e-05 1.32e-05 1.51e-05

SRHGLE 159365.9 121726.9 138881.3

LRLGLE 0.0015 0.0077 0.025

LRHGLE 539404.9 445467 450100.6


Table 4: The statistical features for the Y-Z plan of the three images in the three stages. Stage features

Stage 1Normal image

Stage 2Very Mild AD

Stage 3Mild AD

Y-Z Plane

Pixel Count 14030 13953 14048

Energy 14446103350 14381583696 15632037434

Entropy 48376.72 48579.4 45927.71

Mean Deviation 354.48 333.06 414.19

Root mean square 1014.72 1015.24 1054.87


Skewness -0.36 -0.31 -0.12

Kurtosis -1.11 -0.9 -1.25

Variance 168369.6 150068.04 223268.26

Uniformity 174132 175923 151506

Compactness 1 8.46 8.44 8.46




Cluster Tendency 15393371294.4 14097959365.6 17157369335.2

Dissimilarity 460648.69 452500.54 519766.23

Homogeneity 2 58.97 57.64 58.3

IDMN (Inverse Difference Moment) 4262 4266

SRE 0.304 0.3039 0.3041

LRE 0.63 0.61 0.6

GLN 3.56 3.62 3.12

RLN 3999.23 3971.4 4025.92

RP 0.293 0.293 0.295

LGLRE 4.02e-05 3.98e-05 3.98e-05

HGLRE 277917.94 256991.78 311989.9

SRLGLE 2.63e-05 2.21e-05 2.7e-05

SRHGLE 276551.55 255617.15 310590.86

LRLGLE 0.0005 0.00054 0.0007

LRHGLE 302098.35 288970.12 335845.49


From the previous extracted features it is found that the values of the different extracted features are nearly equal. So, it is very difficult to distinguish between normal and AD patient.

THE FIRST PROPOSED ALGORITHMIn this section, the first algorithm had been proposed to extract the significant features from

AD MRI images to help the doctor easily and fast diagnosis of the AD. A 120 subjects had been examined (49 very mild to mild AD subjects and 71 of non-demented subjects). Figure 4 shows the flow chart of the first proposed algorithm [13].

Figure 4: The First Proposed algorithm flow chart [13].

The Pseudo-code for the first proposed algorithm can be presented as following [13]

1. Read the MRI images.

2. Perform the Preprocessing and Normalization for the input images.

3. Convert 3-D images to 1-D signal.

4. Extract special features using PCA, LDA,

5. Apply the proposed feature reduction and selection method.

6. Apply the proposed feature extraction method.

7. Perform the Cross-validation.

8. Apply classification process using SVM classifier.


Alzheimer’s Disease MRI images

A 3D images from OASIS database [6,7] had been used to extract the most significant features from it. The intensity level (gray level) of each pixel had been used to differentiate among pixels. All images are high resolution and its dimensions are 176 X 208 X 176 pixels.

Preprocessing and Normalization of the Database

The analysis of structural magnetic resonance images is executed by Voxel Based Morphometric (VBM) approaches which permits between- and within-groups differentiation of grey and white matter volume or density. VBM is suitable for large-scale cross-sectional and longitudinal studies which test common age-related neuro-morphologic change. Voxel-based morphometry of MRI data has spatially normalizing all the images to the same space, then extracting the gray matter from the normalized images, smoothing, and finally performing a statistical analysis to localize, and make inferences about, group differences [14-15].

The images before preprocessing was (176 X 208 X 176) pixels and after VBM and preprocessing steps its dimension reduced to (121 X 145 X 121) pixels.

3-D Images conversion to 1D Signals

The 3-D images converted to 1-D signal using reshape Mat Lab function, which is defined as following [16]:

B = reshape (A, SIZ) (1)

The output B is an n-dimensional array with the same elements as A, but reshaped to size (SIZ), a vector representing the dimensions of the reshaped array. The whole images is converted not separated into X or Y or Z axis.

After conversion, each image is presented as a 1-D vector. Each image has a 2122954 pixel. These 1-D signals is applied as an input to the next steps.

Extract Special Features using PCA and LDA

The features had been extracted from the images using the two of the most important feature extraction algorithms PCA and LDA algorithms [17] to compare their results with the proposed feature extraction algorithm.

Proposed Feature Reduction method

The proposed feature reduction step depends on reducing the dimensionality of each image, where each image has 2122945 pixels. These methods based on removing the pixels have the same value in all images and keeping the pixels that have different values in the images. This step is more important and efficient, where the dimensionality of each image is reduces from 2122945 pixels per image to only 690432 pixels per image. The dimensionality of each image is reduced by about 33%. This will save the memory and make the feature extraction is faster and more efficient.


Proposed Feature Extraction method

Ignoring unnecessary features is important to make the comparison between demented and non-demented subjects is more easy, reduce the high dimensionality of each image and increase the accuracy of the classifier without discarding any important information. The proposed feature extraction algorithm is summarized in the following steps:

Step1: Dividing the subjects into two different classes, the first class includes the images of demented (patient) subjects and the other class contains the images of non-demented (Normal) subjects.

Step2: Calculating the mean for each feature in each class where µ1 is the mean of first class and µ2 is the mean of the second class. µ1 will be a matrix of 1 x 690432 features and µ2 will be also 1 x 690432 features.

(2)

Where (n) is number of images in first class and xi represents each image in this class. µ1 is repeated for each feature in the first class i.e. µ1 is repeated 690432.

(3)

Where (m) is number of images in second class and xj represents each image in this class. µ2 is repeated for each feature in the first class i.e. µ2 is repeated 690432.

Step 3: Calculate standard deviation for each feature in each class. Where σ1 will be a matrix of 1 x 690432 features and σ2 will be also 1 x 690432 features.

(4)

(5)

Figure 5 shows the mean µ1 for the first class, the mean µ2 for the second class, the standard deviation σ1 for the first class and the standard deviation σ2 for the second class.

1 1

1 * nii

xn

µ=

= ∑

121 * j

m

jx

mµ

== ∑

1

21 1

1( )n

i inx

σµ==

−

−∑

22

21

1

( )mj

mjy

σµ=

=−

−∑


Figure 5: the mean and standard deviation for the first and the second classes.

Step 4: Maximize the difference between the means of the two classes by making absolute difference between the means of the each feature of the two classes and divide the result by the multiplication of the standard deviation of the each feature of the two classes.

(6)

So, w is the maximum difference between the two classes, µ1 and µ2 are the means and σ1 and σ2 are the standard deviation of first and second class respectively.

Step 5: w is calculated in percent for each feature to understand how much information each feature has.

i=1, 2, …. N features (7)

Where wpercent is calculated for each feature in all images, and wi is the features at image i. Then, w is sorted in descending to sort the features that have high information in the first. Selecting the higher ordered features with higher information will give higher classifier performance with smaller number of features.

Cross Validation

Cross-Validation is an evaluating method that it statistically compare among algorithms by partitioning the data into two groups: the first used for training or training a model and the second used for validating this model. The training and validation groups must successively cross-over in rounds where each data point has a chance to be validated. The k-fold cross-validation is a basic

1 2

1 2*w

µ µσ σ−

=

1

100*percent Nii

www

=

=∑


form of cross-validation. In k-fold cross-validation the data is first partitioned into k equally (or nearly equally) sized segments or folds. Subsequently k iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining k - 1 folds are used for learning. Figure 6 demonstrates an example with k = 3. The darker section of the data are used for training while the lighter sections are used for validation. In data mining and machine learning 10-fold cross-validation (k = 10) is the most common [18,19].

Figure 6: Procedure of three-fold cross-validation.

The cross validation had been used in this work as following:

1. The cross-validation is done after extracting the significant feature from each image and each image is represented with the extracted features.

2. Dividing the images into five groups (5-folds).

3. Cross validation depends on randomly choosing one fold to be used for testing the SVM and the other four folds will be used for training the SVM.

4. Automatically, cross-validation will take another fold to be used for testing and the other folds had been used for training and so on.

5. The cross-validation terminates when each fold had been used only one time for testing the SVM classifier.

Support Vector Machine (SVM) Classification

SVM is used as a classifier and it has gained in popularity in recent years because of its superior performance in practical applications, especially in the field of bioinformatics. A two-class SVM classifier aims to do a hyper plane which maximizes the margin, which is the distance between the closest points on either side of the boundary. These points are known as the support vectors, and their role in the construction of a maximum-margin hyper plane [20, 21].


Performance Evaluation Metric Parameters

This section presents the metric parameters which will be used for measuring the performance of the proposed algorithm. The simplest measurements would be the classification accuracy rate, which is calculated from the number of correctly predicted samples divided by the total number of predicted samples. To test the results the true positive, true negative, false positive and false negative which they are defined as [22,23]:

• True Positive (TP): positive (patient) samples correctly classified as positive (patient).

• False Positive (FP): negative (normal) samples incorrectly classified as positive (patient).

• True Negative (TN): negative (normal) samples correctly classified as negative (normal).

• False Negative (FN): positive (patient) samples incorrectly classified as negative (normal).

Table 5 presents a confusion matrix of several common metrics that can be calculated from it [22,23]:

Table 5: Confusion Matrix.Predicted Class

Actual ClassNegative Class (Normal) Positive Class (Patient)

Negative Class (Normal) True Negative (TN) False Positive (FP)

Positive Class (Patient) False Negative (FN) True Positive (TP)

The metric parameters that will be used to measure the performance of the proposed algorithm are [22,23]:

The Sensitivity (SEN): is also named as Recall or True Positive Rate (TPR) defined as:

(8)

The Specificity (SPE): is also known as True Negative Rate (TNR) is defined as:

(9)

1. The Accuracy (ACC): which is defined as:

(10)

2. Matthews correlation coefficient (MCC): Which is calculate by:

(11)

P

P N

TSENT F

=+

N

N P

TSPET F

=+

N P

P N P N

T TACCT T F F

+=

+ + +

( ) ( )( ) ( ) ( ) ( )

* *

* * *P N P N

P P P N N P N N

T T F FMCC

T F T F T F T F

−=

+ + + +


Experimental Results

This section presents the obtained results of the metric parameters SEN, SPE, ACC, and MCC. There are two experiments that had been executed. The first one, applying the PCA, LDA, and the first proposed algorithm on the 3-D images and comparing the results. The second experiment is applying the first proposed algorithm on the 2D images.

The first experiment

In this experiment, the intensity level (the gray level) of each pixel of the images had been used to differentiate among the pixels. Each image had extremely high dimensionality of features (more than 2 million features).120 images of the normal and AD patients from OASIS database had been used. This algorithm had been compared with PCA and LDA.

Table 6: Sensitivity (SEN) for PCA, LDA, and First Proposed Algorithm.No. of Features

Algorithm 6000 7000 8000 9000 10000

PCA 56.8% 57.8% 58.7% 59.6% 60.9%

LDA 100% 100% 100% 100% 100%

First Proposed Algorithm 94.2% 100% 100% 100% 100%

Table 7: Specificity (SPE) for PCA, LDA, and First Proposed Algorithm.

No. of FeaturesAlgorithm 6000 7000 8000 9000 10000

PCA 68.4% 69.3% 70.3% 71.2% 71.6%

LDA 95.9% 94.7% 93.4% 93.4% 93.4%

First Proposed Algorithm 100% 100% 100% 97.3% 93.4%

Figure 7: Sensitivity (SEN) for PCA, LDA, and First Proposed Algorithm.


Figure 8: Specificity (SPE) for PCA, LDA, and First Proposed Algorithm.

Figure 9: Accuracy (ACC) for PCA, LDA, and First Proposed Algorithm.


Figure 10: Matthews’s correlation coefficient (MCC) for PCA, LDA, and First Proposed Algorithm.

Table 8: Accuracy (ACC) for PCA, LDA, and First Proposed Algorithm.No. of Features

Algorithm 6000 7000 8000 9000 10000

PCA 64.2% 65% 65.8% 66.7% 67.5%

LDA 97.5% 96.7% 95.8% 95.8% 95.8%

First Proposed Algorithm 97.5% 100% 100% 98.3% 95.8%

Table 9: Matthews’s correlation coefficient (MCC) for PCA, LDA, and First Proposed Algorithm.

No. of FeaturesAlgorithm 6000 7000 8000 9000 10000

PCA 24.7% 26.7% 28.7% 30.6% 32.1%

LDA 94.9% 93.2% 91.6% 91.6% 91.6%

First Proposed Algorithm 95% 100% 100% 96.6% 91.6%

Studying the results depicted from Tables 6, 7, 8, and 9, and Figures 7, 8, 9, and 10, it is shown that the different metric parameters (SEN, SPE, ACC, and MCC) reached to the maximum value (100%) using the first proposed algorithm as compared to PCA and LDA with number of features equal to 7000 features.

Other comparison had been done between the first proposed algorithm and the LDA algorithm by trying smaller number of extracted features. The metric parameters values reached to 100% at number of extracted features equal to 2000 features as compared to the LDA, as given in Tables 10 and 11 and Figures 11 and 12.


Table 10: SEN, SPE, ACC, and MCC of the classifier for the first proposed algorithm with small number of extracted features.

No. of FeaturesMetric Parameters 1000 2000 3000 4000 5000

Sensitivity (SEN) 82.7% 100% 100% 96.7% 91.3%

Specificity (SPE) 100% 100% 98.8% 100% 100%

Accuracy (ACC) 93.8% 100% 99.2% 98.3% 95.8%

Matthews’s correlation coefficient (MCC) 83.6% 100% 98.4% 96.9% 92.2%

Table 11: SEN, SPE, ACC, and MCC of the classifier for the LDA algorithm with small number of extracted features.


Sensitivity (SEN) 88.8% 100% 100% 100% 100%

Specificity (SPE) 88.9% 95.4% 95.1% 96.3% 97.5%

Accuracy (ACC) 86.4% 96.7% 96.7% 97.5% 98.3%

Matthews’s correlation coefficient (MCC) 74.7% 93.4% 93.3% 95.1% 96.5%

Figure 11: SEN, SPE, ACC, and MCC of the classifier for the first proposed algorithm with small number of extracted features.


Figure 12: SEN, SPE, ACC, and MCC of the classifier for the LDA algorithm with small number of extracted features.

The second experiment

After the previous experiment, it is noticed that the metrics parameters of the classifier values are 100% with number of features equal to 2000 features. This is a large number and we need to reduce this number of extracted features without reducing the metrics level. This experiment depends on taking one plane from the images (x-y) plane with z at the middle.15 images from OASIS database had been used in this experiment.

The first proposed Algorithm had been applied to the 2D images. Each image dimensionality was 121X145 pixels. First, the images had been converted from 2D to 1D signal. Then, each image was 17545 pixels. Next, the proposed feature selection step had been applied which reduces the number of features to 8236 features. Then, the proposed feature extraction step had been applied to sort the features. Next, cross validation is applied to divide the 15 images into 5 folds. Finally, the loop of five times had been applied to train the SVM with 4 folds and test with the fifth. Table 12 and Figure 13 show the measured values of metric parameters of the classifier using the proposed algorithm for 2D images using intensity (gray) level of the pixels.


Table 12: The metric parameters of the classifier using the proposed Algorithm on 2D intensity (gray) level of pixels.

No. of FeaturesMetric

Parameters250 500 750 1000 1250 1500 1750 2000 2250 2500

SEN 93.3% 100% 86.7% 86.7% 80 % 80 % 80% 80% 60% 76.7%

SPE 80% 100% 60% 60 % 40 % 40 % 40% 30% 16.7% 30%

ACC 93.3% 100% 86.7% 86.7% 80 % 80 % 80% 73.4% 60% 66.7%

MCC 80% 100% 60% 60 % 40 % 40 % 40% 30% 10% 20%

Figure 13: The metric parameters of the classifier using the proposed Algorithm on 2D intensity (gray) level of pixels.

The obtained results presented in Table 12 and Figure 13 indicate that the values of the metric parameters improved to 100% with applying the proposed algorithm using the intensity level of the pixels using number of extracted features equal to 500 features.

Result Discussion

The first proposed algorithm had been applied to the whole 3D images using the intensity (gray) level of the pixels, the features was extremely high (more than 2 million pixels). The metrics of the classifier reached to 100% with number of features equal to 2000 features. This number of extracted features needed to be reduced by applying this proposed algorithm on 2D images (1 plan of 3D images) and the metrics reached to 100% with number of features equal to 500 features. The goal is to reach at high metric parameters (100%) with small number of extracted features.


THE SECOND PROPOSED ALGORITHMThis proposed system depends on extracting the most effective and significant features of

AD images using different discrete transform techniques [24]. The different discrete transforms techniques such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT) are a powerful feature extraction techniques for early and easy diagnosis the AD. There are different approaches that uses different discrete transform techniques with MRI brain images as presented in [25-27].

The proposed AD recognition system indicated in Figure 14 consists of seven stages. These stages are summarized as follows [24]:

1. Preprocessing and Normalization for the images.

2. Converting the 3-D image to 1-D vector.

3. Apply the proposed feature reduction method.

4. Apply different discrete transform techniques Such as DCT or DST or DWT.

5. Apply the proposed feature extraction method.

6. Perform the cross-validation.

7. Perform the classification process using SVM classifier.

Steps 1, 2, 3, 5, 6, and 7 had been discussed previously in the first proposed algorithm. Only step 4 will be discussed in details.


Figure 14: The Second proposed AD recognition system.

Feature Extraction Using Different Discrete Techniques

Significant features can be extracted from the images using different discrete transform techniques such as DCT, DST and DWT.

Discrete cosine transform (DCT)

DCT has been highly used in image processing and signal analysis due to its ‘energy compaction’ property. It compresses most signal information in some coefficients. Considering this, here DCT is chosen for feature extraction. DCT is applied on entire brain image which is in result gives low and high frequency coefficients feature matrix of same dimensions. The most commonly used DCT equation that returns the unitary discrete cosine transform of x [25]:

(12)( ) ( )1

(2 1) 1( ) ( ) cos

2

N

n

n ky k w k x n

Nπ

=

− −= ×

∑


Where x (n) is the input vector for each image, N is the number of features in each image, and w (k) is defined as following [25]:

(13)

Here, N is the length of x, K = 1, 2, 3… N features, and x (n) and y (k) are of the same size.

Discrete sine transform (DST)

The DST is looks like the Discrete Fourier Transform (DFT), but it is using a real matrix. It is equal twice the length of the DFT’s imaginary parts, working on real data where the DFT is imaginary for a real part and odd for odd function. The DST matrix is formed by arranging these sequences row wise. Mathematically, the DST is represented by [26]:

(14)

Discrete wavelet transform (DWT)

The main reasons for using the DWT lie in its complete theoretical framework, the great flexibility for choosing bases and the low computational complexity. The data decomposed into different frequency components by wavelets, then each component is matched to its scale. While the DFT only represent the images based on its frequency component, so it loses time information of the signal [27].


This section presents the comparison study from the obtained results of the metric parameters used for measuring the performance of the second proposed algorithm based on different discrete transform techniques (DCT or DST or DWT).

Tables 13, 14, 15, and 16 and Figures 15, 16, 17, and 18 present the obtained results for the evaluated metric parameters: SEN, SPE, ACC, and MCC of the classifier respectively.

( )

1 1

2 2 k N

forkNw k

N

== ≤ ≤

( ) ( )sin , 1,1 1knNy k x n k Nn N

π = = … = + ∑


Table 13: Sensitivity (SEN) of the classifier using the second proposed algorithm based on different discrete transform techniques.

No. of Features SecondAlgorithm based on 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

DCT 95.5% 100% 100% 100% 100 % 100 % 100 % 100 % 100 % 100 %

DST 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 %

DWT 94.3% 100 % 100 % 100 % 100 % 97.1% 97.1% 91 % 98.3% 96.7%

Table 14: Specificity (SPE) of the classifier using the second proposed algorithm based on different discrete transform techniques.

No. of Features Second Algorithm based on 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

DCT 89% 91.7% 87.9% 90.1% 90.8% 88.1% 88.8% 89.8% 89.5% 88.6%

DST 91.8% 89.8% 83.3% 88 % 87.8% 89 % 87.6% 88.1% 88.6% 87.7%

DWT 94.1% 95.5% 93.8% 92.8% 94 % 95.5% 94 % 93.2% 94.3% 94.3%

Table 15: Accuracy (ACC) of the classifier using the second proposed algorithm based on different discrete transform techniques.

No. of Features Second Algorithm based on 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

DCT 90.9% 94.1% 90.7% 92.5% 93.5% 91 % 91.6% 92.5% 92.6% 91.7%

DST 94 % 92.5% 91.6% 91 % 90.8% 91.7% 90.8% 90 % 91.7% 90.8%

DWT 93.3% 96.7% 95.8% 94.9% 95.8% 95.1% 94.2% 91.6% 94.9% 94.2%

Table 16: Matthews’s correlation coefficient (MCC) of the classifier using the second proposed algorithm based on different discrete transform techniques.

No. of Features SecondAlgorithm based on 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

DCT 81.8% 88.2% 81.4% 85.7% 87.2% 82.6% 83.8% 85.6% 85.6% 84.1%

DST 88.2% 85.1% 83.7% 82.5% 82.5% 83.9% 82.2% 80.5% 83.5% 81.9%

DWT 87.1% 93.4% 91.6% 89.7% 91.9% 90.5% 89.1% 83.5% 89.9% 89 %


Figure 15: Sensitivity (SEN) of the classifier using the second proposed algorithm based on different discrete transform techniques.

Figure 16: Specificity (SPE) of the classifier using the second proposed algorithm based on different discrete transform techniques.


Figure 17: Accuracy (ACC) of the classifier using the second proposed algorithm based on different discrete transform techniques.

Figure 18: Matthews’s correlation coefficient (MCC) of the classifier using the second proposed algorithm based on different.


Result Discussion

The second proposed algorithm based on different discrete transform techniques gives high metric parameters values where:

• The SEN values changes from 91% to 100% as the number of features changed from 500 to 5000.

• The SPE values changes from 81.6% to 95.5% with the variation of the number of features from 500 to 5000.

• The ACC values changes from 85.7% to 96.7% with the variation of the number of features from 500 to 5000.

• The MCC values changes from 70.3% to 93.4% as the number of features varied from 500 to 5000.

THE THIRD PROPOSED ALGORITHMThis proposed algorithm depends on extracting the most effective and significant features of

ADMRI images using the Mel-Scale Frequency Cepstral Coefficients (MFCC) technique [28,29]. MFCC is one of the best feature extraction techniques which used for speech recognition. It hadn’t been used to extract effective features from AD images before [30- 31].

The third proposed AD recognition system indicated in Figure 19, consists of seven steps. The steps of this third proposed algorithm is similar to the steps of the second proposed algorithm except only step 4 which significant features had been extracted using different discrete transforms is replaced by extracting the significant features using MFCC technique. These steps are summarized as follows [28,29]:

1- Preprocessing and Normalization for the images.

2- Converting the 3-D image to 1-D array.

3- Apply the proposed feature reduction method.

4- Extract the most significant features using MFCC technique.

5- Apply the proposed feature extraction method.

6- Perform the Cross-validation.

7- Perform the classification process using SVM classifier.

Steps 1, 2, 3, 5, 6, and 7 had been discussed previously in the first proposed algorithm. Only step 4 will be discussed in details


Figure 19: The third proposed AD recognition system.

Feature Extraction using MFCC Technique

The purpose of using MFCC for image processing is to enhance the effectiveness of MFCC in image processing field. There are different applications in which MFCC used in it specially voice recognition. The MFCC feature extraction steps can be derived as follows [30,31]:

• Perform the Fourier transform of the signal with sampling rate equal to 11025 Hz.

• Map the powers of the spectrum, obtained above onto the Mel scale using triangular overlapping windows.

• Apply the log of power at each of the Mel frequency.

• Perform DCT of the Mel log power just as a signal.

• The MFCC’s are amplitudes of resulting spectrum.

• Number of cepstral coefficients equal to 12.

• Number of filters in filter bank equal to

{3*Log (Sampling Rate)} (15)

The block diagram shown in Figure 20 explains the steps of MFCC process. Steps for calculating MFCC works for 1-D signal. All steps of the MFCC technique is discussed in details in [28,29,32].


Figure 20: MFCC process of 1D AD signal.


This section presents the obtained results of the metric parameters used for measuring the performance of the third proposed algorithm based on MFCC technique and the effect of adding MFCC step on the CAD system. Table 18 and figure21 shows the SEN, SPE, ACC, and MCC of the classifier using the third proposed algorithm using the MFCC step as a function of the number of extracted features.

Table 17: SEN, SPE, ACC, and MCC of the classifier for the third proposed algorithm with MFCC technique.


Sensitivity (SEN) 96.3% 98.3% 100% 100% 100%

Specificity (SPE) 100% 100% 100% 100% 100%

Accuracy (ACC) 98.3% 99.2% 100% 100% 100%

Matthews’s correlation coefficient (MCC) 96.8% 98.5% 100% 100% 100%


Figure 21: SEN, SPE, ACC, and MCC of the classifier for the third proposed algorithm with MFCC technique.

Result Discussion

The third proposed algorithm based on MFCC technique gives high metric parameters values equal to 100% with small number of extracted features (30 features). The number of extracted features needed to realize the high metric parameters values had been reduced by about 70% (from 2000 in the first proposed algorithm to 30 features in the third proposed algorithm (2000/30 = 70%)) which is very significant for the memory size reduction.

COMPARISON STUDY AMONG THE PROPOSED ALGORITHMSThis section presents the comparison study among the proposed algorithms discussed in

sections 4, 5, and 6 under three aspects:

1. Dimensionality and number of extracted features with higher values of metric parameters.

2. The stability of the system at changing the number of extracted features.

3. Processing and Execution time.

The Dimensionality

The classification accuracy depends on the dimensionality .Dimension means the number of features. Table 19 presents the higher values of metric parameters with number of extracted features for all the three proposed algorithms.


Table 18: Dimensionality comparison study among all three proposed algorithms.

Metrics values with No. of Features Algorithm Optimal Metric Parameter Value Required Number of Features

The First Proposed Algorithm

SEN 100 % 2000

SPE 100 % 2000

ACC 100 % 2000

MCC 100 % 2000

Second Proposed Algorithm based on (DCT) technique

SEN 100 % 1000

SPE 91.7 % 1000

ACC 94.1 % 1000

MCC 88.2 % 1000

Second Proposed Algorithm based on (DST) technique

SEN 100 % 500

SPE 91.8 % 500

ACC 94 % 500

MCC 88.2 % 500

Second Proposed Algorithm based on (DWT) technique

SEN 100 % 1000

SPE 95.5 % 1000

ACC 96.7 % 1000

MCC 93.4 % 1000

Third Proposed Algorithm based on (MFCC) technique

SEN 100 % 30

SPE 100 % 30

ACC 100 % 30

MCC 100 % 30

From Table 18 it is depicted that, the metric parameters values equal to 100% at only the first and the third proposed algorithms. From the first proposed algorithm results, it is noticed that the metrics of the classifier values are 100% with number of features equal to 2000 features. From the Third proposed algorithm based on MFCC technique, the values of the metrics parameters of the classifier equal to 100% using the number of features equal to 30 features.

Comparing the two proposed algorithms it is clear that the number of extracted features needed to realize the same value of metric parameters are reduced by about 70% (from 2000 to 30 features = 70%) which is very significant for the memory size reduction and consequently reduces the hardware implementation of the proposed CAD system.

Stability

The stability of the system for the updated data depends on the flexibility of the classifier and the ability of the feature to remain constant over time. A classifier is considered as being stable if bagging does not improve its performance. If small changes of the training set lead to a varying classifier performance after bagging, the classifier is considered to be an unstable one. The unstable classifiers are characterized by a high variance although they can have a low bias. On the contrary, stable classifiers have a low variance, but they can have a high bias [33].


Figures 22 and 23 present the metric parameters values for both the first and the third proposed algorithms.

From Figures 22 and 23, it is depicted that the third proposed algorithm is more stable than the first proposed algorithm as the values of the metric parameters become constant over time as the number of features increases to 30, 40, and 50 features than the first proposed algorithm that the values of the metric parameters changes ups and downs with the increase of the number of features.

Figure 22: The metric parameters values of the first proposed algorithm.

Figure 23: The metric parameters values of the third proposed algorithm discrete transform techniques.


Processing and Execution Time

The speed of the system includes and depends on all the processing or execution time required to perform all the operation stages for the proposed algorithm. The processing time depends on all the number of operation steps performed using the proposed approaches. The execution time (T) can be calculated using the following equation:

T = t1 + t2 + t3 + t4 + t5 + t6 (16)

Where:

t1 is the proposed feature reduction method time.

t2 is the proposed feature extraction method time.

t3 is the descending sorting the extracted features time.

t4 is the cross validation and dividing the images into 10-folds time.

t5 is the training and testing the SVM classifier time.

t6 is the feature extraction using different feature extraction technique time.

Table 19 shows the execution time for different proposed algorithms, the time of each step and the total execution time.

Table 19: The execution time for different proposed algorithms.

Execution Timein sec

Proposed Algorithm

t1(Feature

Selection)

t2(Feature

Extraction)

t3 Descending Sorting)

t4(Cross

Validation)

t5(Training &

Testing SVM)

t6 (different feature

extraction techniques)

Total execution Time (T)

First proposed Algorithm 2.1852 0.7200 0.0533 0.0757 0.6410 0 3.68

Second proposed Algorithm

based on DCT

2.3890 0.7483 0.0075 0.0816 0.7400 0.7871 4.75


based on DST

2.2116 0.7041 0.0080 0.0787 0.6080 0.6167 4.23


based on DWT

2.2020 0.6665 0.0076 0.0793 0.6616 1.0053 4.62

Third Proposed Algorithm

using MFCC

2.1781 0.7270 0.0065 0.0685 0.3721 1.2784 4.63

Figure 24 shows the system runtime analysis of the different three proposed algorithms.


Result Discussion

From the obtained results it is clear that the third proposed algorithm based on MFCC technique gives the metric parameters values equal to 100% with small number of extracted features (30 features).

Comparing this third proposed algorithm with the other two proposed algorithms, it is clear that the number of extracted features needed to realize the same value of metric parameters are reduced by about 70% (from 2000 features at the first proposed algorithm to 30 features (2000/30 = 70%)) which is very significant for the memory size reduction.

The classifier using the third proposed algorithm is more stable than using the first and the second proposed algorithm.

There are negligible increase in the processing or execution time in the second and the third proposed algorithms than the processing time of the first proposed algorithm.

Figure 24: The system runtime analysis of the different three proposed algorithms.


APPLICATION OF THE PROPOSED CAD SYSTEM IN THE AID OF AD DIAGNOSIS

In order to easily apply the first proposed CAD system to help the doctors to fast and easy diagnosis of the AD disease, a simple Graphical User Interface (GUI) had been designed. This GUI will be used to easily get the status of the AD cases which is very difficult to diagnose especially in the early stages of the disease. The doctors use the traditional method for the diagnosis of the AD which depends on the visual looking to MRI brain images and this method isn’t efficient in diagnosis of the AD especially in the early stages. The GUI steps will be discussed as following:

Step1: Applying the first proposed algorithm on several MRI brain images, these images are obtained from:

1. Global and universal Databases.

2. Local patient MRI images in its electronic state

Step2: The obtained images are divided into two parts. The first part used for training the classifier and the second part used for testing the efficiency of the classifier. In this work, the 120 images had been obtained from OASIS database. The first 100 images had been used for training SVM classifier and the last 20 images had been used for testing the SVM classifier. The images are numbered from 1 to 120. The image wanted to be tested, its number must be written in the referred place as shown in Figure 25.

Step3: After writing the image number that wanted to be tested and running the program, the simple GUI program will be appeared with a button written in it “Click Here” and a simple text box has words “Normal or Patient” as shown in Figure 26.

Step4: The state of the patient will be appeared in the text box as “Normal” as shown in Figure 27 (a) or “Patient” as shown in Figure 27 (b) after clicking on the button. The result and the state of the patient will be appeared in the text box in a short time only 3 seconds.


Figure 25: The place where the tested image number must be written in it.

Figure 26: The simple GUI program.


(a) The state is Normal (b) the state is Patient

Figure 27: The state of the patient.

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