Segmentation of Ultrasound Images using Tumour Cut

Volume 4, Issue 3, March 2014
ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Special Issue: Computing Terminologies and Research Development
Conference Held at SCAD College of Engineering and Technology, India
Segmentation of Ultrasound Images using Tumour Cut
Algorithm with KNN Classifier for Breast Cancer
J.Sahaya Regina Mary1,
ME (Communication Systems),
Department of ECE,
Infant Jesus College of Engineering and Technology,
India
1
A.Ahila2
Associate Professor,
Department of ECE,
Infant Jesus College of Engineering and Technology,
India
2
Abstract—Ultrasound also known as sonography, is an effective and important technique. It is used by radiologist for
screening and diagnosis of breast cancer. It is an imaging method using sound waves to look inside a part of the body.
Breast ultrasound is sometimes used to evaluate breast problems that are found during a screening or diagnostic
mammogram or on physical exam. Breast ultrasound is not routinely used for screening. The use of ultrasound
instead of mammograms for breast cancer screening. The detection rate and accuracy of breast cancer depends on the
segmentation of images. Many segmentation algorithms are Region-Growing, K-means Clustering, Edge detection,
Watershed etc. These are used for the detection of tumours and also their advantages and disadvantages are discussed
in this paper. Even the detection rate is still not high. In the proposed method a Tumour-cut algorithm is used for the
segmentation of ultrasound images to increase the detection rate. Noise removal is performed with the help of Gabor
filter. Some features are extracted from the segmented images and classified whether it as benign and malignant using
Kth nearest neighbour classifier (KNN).
Keywords — Breast Cancer, Mammogram, Pre-processing, Segmentation algorithms.
I. INTRODUCTION
Breast cancer [2] is a disease in women among many countries. X-ray sonography is an investigation technique
used by radiologist for screening and diagnosis of breast cancer. Cancer begins in cells. The breast and other parts of the
body are made up of tissues. Usually cells are growing and dividing to form new cells as the body needs them. If the
normal cells grow up old or get dented they will die, and new cells grows on their place. Sometimes new cells began to
form and the body does not need them. The formation of extra cells often forms masses of tissue called lump or tumour.
This type of Cancer forms on breast tissue usually in milk ducts (Tubes that carry milk to nipple) and in the lobules
(glands that make milk). Masses have different shape and will defined boundaries. Masses are mainly classified into
benign and malignant. Benign is the growth of lump and it is not cancerous. The accurate ultrasonic image segmentation
is very important for the exact detection and diagnosis by computer aided clinical tools. Sonography is the most common
investigation technique used by radiologist for screening and diagnosis of breast cancer. A sonogram is an x-ray of the
breast. Sonography is more available, highly accurate and low cost detection method for breast cancer. For this test a
small microphone-like instrument called transducer is placed on the skin. It emits sound waves and picks up the echoes
as they bounce off body tissues. The echoes are converted by a computer into a black and white image on a computer
screen. This test is painless and does not expose you to radiation. This procedure produces a black and white image of the
breast tissue. The image is either on a large sheet of film or as a digital computer image. Many people are worried about
the exposure to x-rays. Advantage of this paper is more available and less expensive.
II. RELATED WORK
Many works have been formulated to develop a segmentation algorithm and diagnosis tools for detecting breast
tumour and classify them whether it as benign and malignant. Samual H. Lewis and Aijuan Dong [1] developed a
Marker-Controlled Watershed segmentation algorithm to locate breast mass tumour candidates. In this method first
selected foreground and background markers from the mammogram images and then applied Watershed segmentation
algorithm to isolate a tumour region from its surrounding tissue. The Marker-Controlled Watershed segmentation was
fairly successful in locating tumour under all conditions. Watershed segmentation is used in various image processing
and computer vision tasks. But the major drawback it is produce false positive results. Arianna Mencattini, Marcello
Salmaeri and Simona Salicone [3] describes a CAD (Computer-Aided Detection) system and diagnosis (CADx) systems
that are widely used in mammography. Both systems are involve the use of computer algorithms to detect patterns in
images associated with signs of the disease and to assign them a malignancy index. The result should attract the
clinicians’ attention to potentially abnormal regions in mammograms. Jawad Nagi, Sameem Kareem and Farrukh Nagi
© 2014, ICCTRD All Rights Reserved
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Mary et al., International Journal of Advanced Research in Computer Science and Software Engineering 4 (3),
March- 2014, pp. 15-18
proposed a Breast profile segmentation method [6]. Breast profile segmentation is an automated technique for
mammogram segmentation. It uses morphological pre-processing and Seeded Region Growing (SRG) to remove
digitization noises, suppress radiopaque artefacts and remove the pectoral muscle to accentuate the breast profile region
for use in CAD algorithms. SRG performs segmentation of an image with respect to a set of points, known as seeds. The
algorithm has been tested using mammogram images of differing densities from multiple databases and has shown results
with accuracy. Kekre.H.B, Saylee M. Gharge and Tanuja K. Sarode [7] proposed an algorithm. The algorithm uses
probability of mammographic image as input for vector quantization. Kekre’s Proportionate Error (KPE) algorithm is
used for region forming, and codebook of size 128 is created. Further the 128 clusters were utilized for region merging
using KPE algorithm for reclustering. The tumour sectional area is calculated and centre point is compared with LindeBuzo-Gray (LBG) algorithm for segmentation of mammographic images. The probability of original image is used for
grouping pixels into regions and then the image of probability is formed. For image segmentation Equalized probability
image is used as an input image for further segmentation. Roshan Dharshana, Yapa and Koichi Harada [9] proposed
Breast skin-line estimation and breast segmentation techniques. An image would increase the accuracy and efficiency of
processing algorithms. Leonardo de Oliveira Martins and Geraldo Braz Junior[8] described a Clustering algorithms that
can be applied to solve the segmentation problem. It consists in choosing an initial pixel or region that belongs to one
object of interest, followed by an interactive process of neighbourhood analysis, deciding if whether each neighbouring
pixel belongs or not to the same object. K-means algorithm is used to resolve the mass detection task on mammograms
using texture information obtained from Haralick’s descriptors. The K-means algorithm is one of the simplest nonsupervised learning algorithms classes that solve the clustering segmentation problem. The method follows the usual
steps to satisfy the primary objective that is clustering all the image objects into K distinct groups. First, K centroids are
defined, one for each group. Their initial position is very important to the result. After that, it is determined a property
region for each centroid, which groups a set of similar objects. Maanasa N A S, V Gowri proposed a tumor cut
segmentation method and also using Support Vector Machine Classifier to classify whether the semented image as benign
and malignant.
III. METHODOLOGY
The detailed architecture of ultrasound segmentation. The proposed Tumour-Cut segmentation method consists
of a few phases as shown in Figure 1.
Ultrasound images
Image Pre-Processing
Segmentation
Feature Extraction
Classification
Benign
Malignant
Fig1: Architecture.
A. IMAGE PRE-PROCESSING
Ultrasound images are x-ray images of breast and it is difficult to interpret. To improve the quality of sonogram
pre-processing stage is essential. Digitization noises and high frequency components are some unwanted data in
ultrasound images. These can be removed by using Gabor filter. Gabor filter remove the noises without disturbing the
edges of the ultrasound images.
B. SEGMENTATION
To extract the mass contours from the ultrasound segmentation. Tumour-Cut algorithm is proposed to partition
the ultrasound image into different segments. The segmentation phase produces ultrasound image segments within 500
iterations. Using fuzzy c-means clustering to segment the filtered image.
C. FEATURE EXTRACTION
In this stage a set of features related to the geometry of the boundary and the structure inside it are selected.
Almost 16 parameters are considered for feature extraction. It includes gray level co-occurrence matrices (GLCM) and
© 2014, ICCTRD All Rights Reserved
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Mary et al., International Journal of Advanced Research in Computer Science and Software Engineering 4 (3),
March- 2014, pp. 15-18
haralick features. The GLCM features computed are contrast, correlation, entropy, homogeneity. The haralick features
such as ASM, SOSV, IDM, SUMA, SUMV, SUME, IMC1, IMC2, differentiation of variance, differentiation of entropy,
MCC, entropy are also calculated. After features are extracted a feature selection is needed to extract an optimal subset of
features for classification. It is done by the help of ANOVAs test calculator available in the internet.
D. CLASSIFICATION OF TUMOR
The classification stage classifies the tumour masses into two types. They are benign and malignant. The benign
masses are just a growth of cells but not cancerous. Malignant masses are cancerous tumours. For classification KNN
classifier is used. The idea is minimizing the generalization error. If the classifier is applied to test samples that do not
exactly match any training sample used to train the classifier.
IV. EXPERIMENTAL RESULTS
From this procedure some ultrasound images are benign and malignant images. The fig [2] shows the original
ultrasound image. A Gabor filter is applied to the original ultrasound and filter the unwanted noises in the images.
Digitalization noises and high frequency components are removed by the filter. The noise filtered ultrasound image is
shown in fig [3]. The pre- processed images are then segmented using the Tumour-cut algorithm. The c-means clustered
image is shown in fig [4]. Then the segmented image is shown in figure [5].After that GLCM and haralick features are
extracted from the segmented images. The GLCM features computed are contrast, correlation, entropy, homogeneity. The
haralick features such as ASM, SOSV, IDM, SUMA, SUMV, SUME, IMC1, IMC2, differentiation of variance,
differentiation of entropy, MCC, entropy are also calculated. Finally classify the tumour as benign and malignant using
KNN classifier. The calculated features are shown in figure [6].
Fig. 2 Original Ultrasound image
Fig.4 Clustered image
© 2014, ICCTRD All Rights Reserved
Fig. 3 Filtered Ultrasound image
Fig.5 Segmented image
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Mary et al., International Journal of Advanced Research in Computer Science and Software Engineering 4 (3),
March- 2014, pp. 15-18
V. CONCLUSION
Breast cancer is the most common disease. It is one of the major causes of death among women. The early
detection of cancer will reduce the mortality rate and provide more treatment options. Sonography is the most common
investigation technique used by the radiologist for the screening and diagnosis of breast cancer. It is highly accurate and
low cost detection method. Many segmentation algorithms are used for the detection of breast tumours. Even the
detection rate is still not high. For this a Tumour-cut algorithm is proposed for the segmentation of ultrasound images to
increase the detection rate. Digitization noise and artefact removal is performed by using with the help of Gabor filter. It
will also remove false positive results. GLCM and haralick features are extracted from the segmented ultrasound images.
A subset of features is selected and performed classification of tumour masses with KNN classifier. The experiment
shows a 99% detection rate and only produces only 1% false positive identification. This work can be extended to
generate strong results by applying this TC algorithm.
REFERENCES
[1]
Samual H. Lewis and Aijuan Dong (2012) ’Detection of Breast Tumor Candidates Using Marker-controlled
Watershed Segmentation and Morphological Analysis’ IEEE Trans. Instrum. Mea. vol. 59.
[2]
American Cancer Society (2011) ‘Cancer facts and figures’.
[3]
Arianna, Marcello Salmaeri and Simona Salicone (2010) ’Metrological characterization of a CADx System for the
Classification of Breast Masses in Mammogram’ IEEE-Trans 0n Instrumentation and measurement Vol.59.
[4]
C.J.C. Burges (1998) ‘A Tutorial on Support Vector Machines for Pattern Recognition’ Kluwer Academic
Publishers.
[5]
Janet.J,S and Meenalosini (2012) ’Segmentation of cancer cells in mammogram using Region growing Method’
International Journels of Engineering research and Application. Vol.2.
[6]
Jawad Nagi, Sameem Abdul Kareem and Farrukh Nagi (2010) ’Automated Breast Profile Segmentation for ROI
Detection Using Digital Mammograms’ IEEE EMBS Conference on Biomedical Engineering & Sciences
[7]
Kekre.H.B, Saylee M. Gharge and Tanuja K. Sarode (2010) ’ Image Segmentation of Mammographic Images
Using Kekre’S Proportionate Error Technique On Probability Images’ International Journal of Computer and
Electrical Engineering Vol.2, No.6.
[8]
Leonardo de Oliveira Martins and Geraldo Braz Junior (2009) ’Detection of Masses in Digital Mammograms
using K-means and Support Vector Machine’ Electronic Letters on Computer Vision and Image Analysis 8(2):3950.
[9]
Roshan Dharshana and Yapa and Koichi Harada (2007)’ Breast Skin-Line Estimation and Breast Segmentation in
Mammograms using Fast-Marching Method’ International Journal of Biological and Life Sciences.
[10] C.C Chang,C.J. Lin ‘A library for support vector machines’, available at httpwww.csie.ntu.edu.tw/cjlin/libsvm.
[11] Maanasa N A S, V Gowri (2013) ‘Segmentation of mammogram using Tumour cut algorithm’ International
Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 10.
AUTHOR’S PROFILE
Sahaya Regina Mary.J completed her B.E degree in Electronics and Communication Engineering
from Francis Xavier Engineering College, Anna University in 2012. She is currently pursuing her
Master of engineering in Communication Systems from Infant Jesus College of engineering and
Technology, Thoothukudi in 2014. Her area of interest includes Medical Image processing, Digital
Image Processing, Optical Communication, Wireless networks, Mobile computing Communication
networks and Embedded Systems.
Ahila.A received her B.E degree in Electronics & communication engineering from National
Engineering College, Kovilpatti and M.E degree in Applied Electronics from Government College of
Engineering, Tirunelveli and currently pursuing her PhD under the Supervision of Dr.S.Allwin. Her
area of interest is Digital image processing, Medical Image processing embedded systems, Wireless
Communication, Optical Networks.
© 2014, ICCTRD All Rights Reserved
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