Brain CT-Scan Classification Using PCA and K-NN

ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE)
Volume 1, Issue 6, August 2012

Brain CT-Scan Images Classification Using PCA,
Wavelet Transform and K-NN
Kamaljeet Kaur, Daljit Singh

Abstract: With rapid development of technology in It is primarily used to detect and locate structures inside
biomedical image processing, classification of tissues of human the body that cannot be located by other forms of
body is very challenging task as it requires very accurate radiological investigation. CT-Scan produce images of
results without any misclassification. By making use of this
tissues, shows the exact location of structures within soft
technology along with neural network; a hybrid technique has
tissues. It is useful to detect bleeding within skull or tumors.
been proposed for classification of Brain CT-Scan images.
This technique is not limited to medical field; it is also
Traditional X-rays suffer from their inability to give any
applicable to classification of natural images. Database
perception of depth to the physician. CT-Scan is better than
consists of CT-Scan images and Brodatz texture. The
methodology adopted in this paper consists of two stages: traditional X-rays in three different ways:
firstly, features are extracted from given images using feature
extraction algorithms PCA and Wavelet Transform. They are - CT-Scan uses a very narrow beam of X-rays that can
further fed as an input to train the K-NN classifier to classify penetrate straight through the body in a straight line to
between normal and abnormal images. For Brain CT-Scan the detector.
images; features extracted by PCA gives 100% classification - X-Ray source is rotated around the body so that the X-
accuracy with execution time of 0.6133 seconds whereas for rays pass through the entire structure in both directions.
Brodatz texture images; features by Wavelet transform gives - A computer is used to reconstruct the intensity of X-
classification accuracy of 100% with execution time of 0.1912 rays into an image showing the density of any point of
seconds. Code is developed by using MATLAB 2011a.
the plane through which X-rays passed.
Index Terms: CT-Scan, PCA, GLCM, K-NN, feature
extraction II. METHODOLOGY
Several feature extraction algorithms are in use. In this
I. INTRODUCTION paper, authors have used only Principal Component
Computed tomography (CT-Scan) is a medical Analysis (PCA) and Wavelet Transform algorithms for
imaging procedure which utilizes computer-processed X- feature extraction from images. These extracted features are
rays to produce tomographic images or 'slices' of specific used as input to train the K-Nearest Neighbor (KNN)
areas of the body including soft-tissues. These three- classifier which give results as normal and abnormal
dimensional images of interior body tissue are used for images.
diagnostic and therapeutic purposes in various medical
disciplines. Digital geometry processing is used to generate III. DATABASE
a three-dimensional image of the inside of an object from a Database used in this paper consists of different sets of
large series of two-dimensional X-ray images taken around normal and abnormal CT-Scan images of different patients.
a single axis of rotation. It consists of 17 images in train dataset and 7 images in test
dataset. For Brodatz texture; it consists of 9 texture images
in train dataset and 9 images in test dataset. Images are
Kamaljeet Kaur, Electronics and Communication Engineering, Ludhiana defined with their class labels. Below fig.1 and fig.2 shows
College of Engineering and Technology, Ludhiana, India. brain CT-Scan images normal and abnormal respectively
whereas fig.3 and fig. 4 shows images for Brodatz texture.
Daljit Singh, Electronics and Communication Engineering, Ludhiana
College of Engineering and Technology, Malerkotla , India, 9465378987.

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All Rights Reserved © 2012 IJARCSEE

ISSN: 2277 – 9043

Figure 1: Normal Brain CT-Scan image Figure 4: Brodatz texture image

IV. FEATURE EXTRACTION
Feature extraction involves simplifying the amount of
resources required to describe a large set of data accurately.
When performing analysis of complex data one of the
major problems stems from the number of variables
involved [3]. Analysis with a large number of variables
generally requires a large amount of memory and
computation power or a classification algorithm which over
fits the training. Feature extraction is a general term which
depicts to extract only valuable information from given raw
data. The main objective of feature extraction is to represent
Figure 2: Abnormal Brain CT-Scan image
raw image in its reduced form and also to reduce the
original dataset by measuring certain properties to make
decision process easier during classification.

A. Principal Component Analysis (PCA)
PCA is a transformation that converts the set of
correlated variables into set of uncorrelated variables. The
goal of PCA is to reduce the dimensions of data by making
it computationally feasible, while retaining as much as
possible of variation present in original dataset. It is used
for first order feature extraction. Features extracted using
PCA are: Mean, Variance and Standard-Deviation
Figure 3: Brodatz texture image
Following steps are to be followed while using PCA:

Step 1: Get two-dimensional data-set to understand the
concept of PCA.

Arrange the data as a set of N data vectors X1…….XN with
each XN representing a single grouped observation of the M
variables.

- Write X1……… XN as column vectors, each of which
has M rows.

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- Place the column vectors into a single matrix X of B. Haar Wavelet Transform
dimensions M x N. Transform of signal is another form of representation of
signal but doesn‟t change the information content present in
Step 2: Calculate the Mean
signal. Transformation of signal is done to obtain the
For PCA to work properly on data; subtract the mean from further information which is not present in raw signal. The
each of the data dimensions. Subtracted mean is the average wavelet transform decomposes the image into a set of
across each dimension. different resolution sub-images, corresponds to various
frequency bands. Basic idea of DWT is to approximate a
- Calculate the mean along each dimension m= 1… M.
signal through a set of given mathematical functions [1].
- Store the calculated mean values into vector „u‟ of
This yields a multi-resolution decomposition of signal into
dimensions Mx1.
four sub-bands called approximation and details.
𝑁
1 ∞
𝑢 𝑚 = 𝑋(𝑚, 𝑛)
𝑁 𝑦𝑙𝑜𝑤 𝑛 = 𝑥 𝑘 𝑔[2𝑛 − 𝑘]
𝑛=1
𝑘=−∞
Step 3: Calculate Zero Mean data ∞

- Subtract the empirical mean vector „u‟ from each 𝑦ℎ𝑖𝑔ℎ 𝑛 = 𝑥 𝑘 ℎ[2𝑛 − 𝑘]
column of the data matrix X. 𝑘=−∞

- Store zero mean data in the M × N matrix B. Haar is a simplest wavelet and is a sequence of rescaled
B = 𝑋 − 𝑢ℎ “square shaped” functions which together form a wavelet
family [6]. Haar wavelet‟s mother wavelet function Ψ (t) can
Step 4: Calculate the Covariance Matrix „C‟. be described as:

- Find the Covariance Matrix C from matrix B with 1, 0 < 𝑡 < 1/2
itself. 𝜑(𝑡) = −1, 1/2 < 𝑡 < 1
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
As given data is two-dimensional thus covariance
matrix is also two-dimensional. If the non-diagonal Its scaling function ∅(𝑡) can be described as:
elements of co-variance matrix are positive, both
variables increase together. If the non-diagonal −1, 0< 𝑡<1
∅ 𝑡 =
components are zero; then variable are independent of 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
each other and are uncorrelated.
Haar wavelet decomposes an high-frequency
Step 5: Find Eigenvectors and Eigenvalues of Covariance components into further four sub-bands, LL, LH, HL, HH
Matrix C which is a square matrix.
resp. LLk, HLj , LHj , HHj ; where j= 1,2,….k
- Compute the matrix V of Eigen vectors which
diagonalizes the covariance matrix C. K denotes the image‟s decomposition scale levels of
wavelet transform ,LLk denotes the kth level low-frequency
Eigenvectors provide the information about patterns in data sub-image and HLj, LHj, HHj denote the jth scale high
and are perpendicular to each other. frequency sub-images, where HL indicates variation along
Step 6: Rearrange the Eigenvectors and Eigenvalues in X-axis and LH indicates variation along Y-axis. Power is
decreasing order. more compacted in LL band. In order to obtain more image
detail information, decompose further high-frequency
Step 7: Derive a new dataset. components using haar wavelet transform. It helps to obtain
more detailed information in all level sub-images except
Dimensions are greatly reduced and most representative
low-frequency sub-images.
features of whole dataset still remain within only selected
Eigen features. {HLj00, HLj01 HLj10 HLj11}
{LHj00, LHj01 LHj10 LHj11}
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{HHj00, HHj01 HHj10 HHj11} into two phases: training phase and testing phase. In
training phase; known data is given and in testing phase;
Where j= 1, 2…. K and j00, j01, j10, j11 denote the unknown data is given. Classification is done by using
position of four sub-images. classifier after training.

A .K-Nearest Neighbor (K-NN)
K-NN is a method for classifying objects based on
closest training examples in feature space. K-NN is a type
of instance based learning where the function is locally
approximated. This is simplest of all machine learning
algorithms. An object is classified by majority vote of its
neighbors, with the object being assigned to class most
common among its K nearest neighbor.

K-Nearest Neighbors (KNN) classification divides given
data into a training set and a test set. For each row of the
test set; the K nearest (in Euclidean distance) training set
objects are found, and the classification is determined by
majority vote with ties broken at random [7]. If there is tie
for the 𝑘 𝑡ℎ nearest vector, all candidates are included in the
vote. The training examples are vectors in a
multidimensional feature space, each with a class label. The
Figure 5: Wavelet decomposition training phase of the algorithm consists only of storing the
feature vectors and class labels of the training samples. In
Above process shown in fig. 5 depicts how the image is
the classification phase, k is a user-defined constant and any
decomposed into different sub-bands.
query point is classified by assigning the label which is
C. Features extracted from images: most frequent among the k training samples nearest to that
query point.
There are several features in images which are able to
represent whole image if extracted carefully. In this work; By default, Euclidean distance method is used to
only three features are extracted and they are described calculate the distance between query points with k=1. In
below: case of text classification; hamming distance can be used.

Mean: It is an average value and measures the general
VI. FOLLOWED STEPS FOR FEATURE EXTRACTION
brightness of an image.
Variance: It is a measure of spread of data in data set. AND CLASSIFICATION
Covariance between one dimension and itself is called as As shown in fig.6; the proposed methodology is divided
variance. into two phases. They are discussed in this section.

Standard Deviation: It calculates the average distance from A. Training Phase
mean of the data set to a point. 1. Obtain any image and convert it into matrix form.
Store the resultant image in the form of array; it can
be 2-dimensional or 3-dimensional depending upon
the image. Matrix can be of class single, double,
V. CLASSIFICATION
uint8, uint16.
Classification refers to the analysis of the properties of 2. Any image read by MATLAB is in RGB format, but
an image. Depending upon the analysis; the dataset is RGB format is difficult to manipulate as pixel values
further referred into different classes. Input features are range from 0-65535. So, convert the matrix into gray
categorized as 0 and 1.The classification process is divided image; where pixel values range from 0-255. 0
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represents black pixel and 255 represents white pixel.
In between values represents different gray levels. Start
3. Apply an algorithm developed using PCA on matrix.
It transforms the data into uncorrelated form by
reducing redundant variables from matrix. It is done Brain CT-scan images as
by calculating the variables having large amount of input
variance. Extract the required features from newly
derived dataset.
4. Now, apply wavelet transform on matrix. Decompose Feature extraction
the image into sub-image .They are further
decomposed into four sub-bands LL,LH,HL,HH and
provide values approximation detail, horizontal detail,
Features by PCA Features by Wavelet
vertical detail and diagonal detail. Out of these
Transform
approximation details have been selected for feature
extraction.
5. Save the above extracted features in your system with
K-NN Classifier
.mat extension for further process.
6. Now, train the K-NN classifier using extracted
features for classification. This can be done by
Decision
applying the features as an input to the classifier. It
classifies between images by calculating the distance
between test data and train data. Also by changing the Normal Abnormal
value of K. In this thesis; K=1 have been considered
by using Euclidean distance as distance method.
Having large number of features in train data; Stop
classifier will get trained more and leads to better
accuracy.

B. Test Phase Figure 6: Proposed Methodology
7. Next step is to test the performance of classifier by
using test images. Repeat the steps as discussed in VII. RESULTS
points 1 to 5. Apply the test data to classifier. It As discussed above; three features have been extracted
differentiates between images by calculating the from images using two different feature extraction
Euclidean distance between the data points and value algorithms. In Brodatz texture classification; out of 9, 8
of K selected is 1. Images having minimum distance images are correctly classified with 1 misclassification
with train images are taken as they belong to the same when features are extracted by PCA. Similarly when
class. If not; their distance are further calculated with features extracted by Wavelet transform; all 9 images are
another image in train data. correctly classified without any misclassification.
8. Same process is repeated for Brodatz texture images.
Below shown tables gives values for normal images for
The flow chart gives an idea of steps followed during variance and standard deviation. Images having feature
training phase and test phase for feature extraction and values beyond these ranges are considered as abnormal
classification for both medical images and Brodatz texture. images. During classification process; values of standard
deviation have been given more importance. The same
results are also shown in graphical form.

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TABLE I: Classification results for Brain CT-Scan images
with feature values by PCA

FEATURES BY PCA MAXIMUM MINIMUM

STANDARD DEVIATION 19.2869 17.9485

VARIANCE 3447.8268 1456.4312

TOTAL IMAGES IN TEST DATASET 7

ACCURATELY CLASSIFIED BY K-NN 7

TABLE II: Classification results for Brain CT-Scan images Figure 8: Standard Deviation by PCA
with feature values by Wavelet Transform

FEATURES BY
MAXIMUM MINIMUM
WAVELET TANSFORM

STANDARD DEVIATION 108.000 77.000

VARIANCE 12897.6728 6794.5950

TOTAL IMAGES IN TEST DATASET 7

ACCURATELY CLASSIFIED BY K-NN 6

A. Graphs

Figure 9: Variance by Wavelet Transform

Figure 7: Variance by PCA

Figure 10: Standard Deviation by Wavelet Transform

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The above shown graphs represent the values of [3] M.Vasantha, “Medical Image Feature Extraction, Selection and
Classification”, International Journal of Engineering Science and
variance and standard deviation in the form of bars for
Technology, Vol.2 (6), 2010, 2071-2076.
normal and abnormal Brain CT-Scan images extracted by
PCA and Wavelet Transform. [4] Manimegalai.P, Revathy.P, Dr.K.Thanushkodi, “Micro-calcification
Detection in Mammogram Image using Wavelet Transform and Neural
VIII. CONCLUSION Network” .International Journal of Advanced Scientific Research and
Technology. Issue2.Volume 1(February 2012). ISSN: 2249-9954.
PCA and Wavelet transform are very efficient tools for
feature extraction and they are very successfully used in [5] R.Nithya, B.Santhi, “Comparative Study on Feature Extraction Method
for Breast Cancer Classification” Journal of Theoretical and Applied
biomedical image processing. In this paper, classification Information Technology, 30 Nov, 2011. Vol.33 No.2, ISSN: 1992 - 86
technique is developed to automatically detect whether an
abnormality in CT-Scan exists or not. If the relevant [6] Ms.Yogita K.Dubey, Milind M. Mushrif, “Extraction of Wavelet Based
features are successfully extracted from brain CT-Scan Features for Classification of T2-Weighted MRI Brain Images”. Signal
and Image Processing: International Journal (SIPIJ) Vol.3, No.1, February
images; they can help in detection of abnormalities in 2012.
human body at very initial stage which helps to save the
previous human life. Same features are extracted by both [7] N.Suguna, Dr. K.Thanushkodi, “An improved K-nearest neighbor
classification using Genetic Algorithm” IJCSI- International Journal of
algorithms. After classification; the performance of
Computer Science Issues, Vol.7 , Issue-4, July 2010. ISSN: 1694-0784
classifier is discussed below with both PCA and Wavelet
Transform with same features. [8] Ryszard S.Choras, “Image Feature Extraction Techniques and Their
Applications for CBIR and Biometrics Systems”, International Journal of
For Brodatz Texture images; features by PCA gives Biology and Biomedical Engineering. Issue 1, Vol.1, 2007.
maximum classification accuracy of 88.88% with execution
time of 0.7480 seconds. On the other hand; features
extracted by Wavelet Transform gives maximum accuracy
of 100% with execution time of 0.1912 seconds.
Kamaljeet Kaur is pursuing her M-Tech (regular)
For Brain CT-Scan images; features by PCA gives thesis in Biomedical Image Processing. She had
maximum classification accuracy of 100% with execution completed her B-Tech in Electronics and
Communication Engineering in 2009.She has
time of 0.6133 seconds. Similarly features extracted by attended 2 national conferences on image
Wavelet Transform give maximum classification accuracy processing. She had published 3 national papers
of 85.71% with execution time of 0.5508 seconds. and 1 international journal. Her areas of interest
are Digital Image Processing and
Distance method used to calculate the distance between Microcontrollers.

train points and test points are Euclidean method. From the
above discussion; it has been concluded that brain CT-Scan
images can be accurately classified if K-NN is used in Daljit Singh is pursuing his M-Tech (regular)
combination with PCA. Similarly; Brodatz texture can be thesis in Biomedical Image Processing. He had
accurately classified if K-NN is used in combination with completed his B-Tech in Electronics and
Communication Engineering in 2010. He had
Wavelet Transform.
published 3 national papers and 1 international
journal. He has attended several conferences
REFERENCES
on microcontrollers and image processing. His
areas of interest are Digital Image Processing,
[1] Amir Rajaei, Lalitha Rangarajan, “Wavelet Based Feature Extraction
Signal Processing and Microcontrollers.
for Medical Image Classification”. An International Journal of Engineering
Sciences, ISSN: 2229-6913 Issue Sept 2011, Vol.4.

[2] EL-Sayed, EL- Dahshan, Abdul- Badeeh M. Salem, Tamer H.Yousin,
“A Hybrid Technique for Automatic MRI Brain Images Classification”.
Studia Univ. Babes.Bolyai, Volume LIV, Number 1, 2009.

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