Download - aes journals

JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
BREAST TUMOR ANALYSIS USING TEXTURE
FEATURES AND WAVELET TRANSFORM WITH
DYNAMIC NEURAL NETWORK BASED TRAINING
1
1, 2
H K SAWANT, 2 VAISHALI D. SHINDE
Department of Information Technology, Bharati Vidyapeeth Deemed University
College Of Engineering, Pune-46
[email protected]
ABSTRACT : Most of the women in India suffer from Breast cancer. It is the common malignant disease. If it is
early detected and characterized it will help reduce the need for therapeutic treatment and minimizes pain and
suffering. The tumor of Breast cancer that starts from cells of the breast. A malignant tumor is a group of
cancer cells that grow into surrounding tissues .Also spread to distant areas of the body. The disease occurs
almost entirely in women, but men can get it, too. Breast cancer classification includes grading, staging, and
determination of the hormonal status of cancer. This information helps the physician to plan treatment,
determine your prognosis, evaluate the results of cancer treatment, and standardize communication among
healthcare providers for consultation, referral, and research. Grading is based on the microscopic structure of
the tumor. It is the visual appearance of the cells. The pathologist is finds out how the cell compares to normal
cells. Normal cells are well differentiated and are less aggressive. Staging involves the measurement of the
tumor, lymph node involvement, and the metastatic spread (TNM).
Key Words:. Breast Cancer, Malignant Tumor, Mammogram, Dce-Mri Using Texture Features And Wavelet
Transform
1. INTRODUCTION:
Currently high-quality mammography is the most
effective technology available for breast cancer
screening. But Mammography presents images in 2 D
projective view and has limited sensitivity and
specificity. The mammograms can detect malignant
tumors that cannot be felt but treating a small tumor
does not always mean that a woman’s life will be
saved. The development of a vascular supply will
decide the growth of the tumour, which induces the
growth of new blood vessels. The tumor malignancy
is highly correlated with vascularity.
Magnetic resonance imaging (MRI) is a
diagnostic study which makes pictures of organs of
the body using magnetic field and radio frequency
pulses.Dynamic
Contrast
Enhanced-Magnetic
Resonance Imaging (DCE-MRI) uses fastest imaging
and constrasting agent. Contrast material substance is
given by vein. Also DCE-MRI gives us extra
information which is not available with the regular
MRI. The regular MRI shows only pictures of the
tumor while the DCE-MRI gives information about
the blood vessels of the tumor. Intensity values at
individual pixel lead to image noise, motion artifact
and MR artifact. So we are going to use texture
features which are the most important image
attributes to identify and compare regions.Then we
will train the neural network with the acquired
temporal sequence. Each classification texture feature
will correspond to classification of breast cancer. We
can use this trained machine to find the classification
of tumour of new patient.
This distinguishes itself from other similar research
efforts in the following:
a) Use of high quality DCE-MRI image.
b) Saving of all pixels attributes using texture
analysis.
c) Powerful machine learning using Neural
Networks.
d) The project is a complete system for finding out
the stage of breast cancer of a woman.
2. GOALS & OBJECTIVE:
The application is primarily intended to be used to
acquire the DCE-MRI of breast.It is acquired from
the Radiology Department of Cancer Specialist
hospital.Then apply segmention, so we will get the
breast region separated from chest region.Then on
this segmented region apply texture analysis and find
out the various texture attributes like run length
matrices and histogram statistics.Then using this
texture values train the neural network.The neural
network will get trained according to the
classification of that type of breast cancer. On this
trained NN the new image with unkown classification
will be tested and the stage of cancer can be found
out.
2.1 Basic Concepts
a) Texture Features
Texture Features at the pixel can alleviate the impact
of artifact since it is based on large neighborhood and
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 46
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
not pixel to pixel correspondence. It is also different
from smoothing operator since it preserves all the
pixel attributes. Texture features are much more
stable, so they can be used to address different image
protocols much better. They are one of the most
important image attributes to identify, characterize,
and compare regions with similar properties and to
distinguish different patterns. Texture analysis is
important in distinguishing benign and malignant
breast lesions.
b) Pattern Recognition
Pattern recognition is an important application of
neural networks. Pattern recognition can be
implemented by using a feed-forward neural network
that has been trained accordingly. The network is
trained to associate outputs with input patterns. When
the network is used, it identifies the input pattern it
then tries to output the associated output pattern. The
power of neural networks is when a pattern that has
no output associated with it, is given as an input. In
this case, the network gives the output that
corresponds to already trained input pattern that is
least different from the given pattern.
c) Machine Learning
Machine learning is about learning structure of data.
We are going to use Single Layer Neural Networks.
We will train the neural network with the acquired
temporal sequence. Each classification texture feature
will correspond to classification of breast cancer. We
can use this trained machine to find the classification
of tumour of new patient.
There are following types of learning:
d) Supervised learning which incorporates an
external teacher, so that each output unit is told what
its desired response to input signals ought to be.
During the learning process global information may
be required. Paradigms of supervised learning include
error-correction learning, reinforcement learning and
stochastic learning. Important issue conserning
supervised learning is the problem of error
convergence, i.e. the minimisation of error between
the desired and computed unit values. The aim is to
determine a set of weights which minimises the error.
One well-known method, which is common to many
learning paradigms, is the least mean square (LMS)
convergence.
e) Unsupervised learning is based upon only local
information. It is also referred to as self-organization,
as it self-organizes data presented to the network and
also detects their emergent collective properties.
Paradigms of unsupervised learning are Hebbian
learning
and
competitive
learning.
From Human Neurones to Artificial Neuronesther
aspect of learning concerns the distinction or not of a
seperate phase, during which the network is trained,
and a subsequent operation phase. We say that a
neural network learns off-line if the learning phase
and the operation phase are distinct. A neural
network learns on-line if it learns and operates at the
same time. Usually, supervised learning is performed
off-line, whereas unsupervised learning is performed
on-line.
2.2 System Flow:
a) Traing Part of neural network:
1. The breast cancer images with the known stage i.e.
classification are stored in one
table. This table also contains the extracted feature
values of the images.
2. This data is fed to neural network.
3. The network gets trained for these classifications.
b) Testing of new image:
1. The cancer image with unknown type of
classification is taken.
2. Using segmentation the breast region is separated
from chest region.
3. On this segmented region the texture analysis is
applied.
4. The extracted feature values are fed to neural
network.
5. On the trained network the new image is tested to
find its classification.
6. The classification of the cancer is given as output.
3. LITERATURE ANALYSIS:
Presently available technology for breast cancer
screening is High-quality mammography. Many
efforts are made to improve mammography focus on
refining the technology and improving how it is
administered and x-ray films are interpreted. NCI
(National Cancer Institute) is funding research to
reduce the already low radiation dosage of
mammography; enhance mammogram image quality;
develop statistical techniques for computer-assisted
interpretation of images; enable long-distance,
electronic image transmission technology for clinical
consultations; and improve image-guided techniques
to assist with breast biopsies. NCI also supports
following research technologies. They do not use xrays. They are Magnetic Resonance Imaging (MRI),
Ultrasound, and breast-specific Positron Emission
Tomography (PET) are used to detect breast cancer.
3.1 Mammogram
A mammogram [6] is a technique which takes an xray picture of the breast.It can be used to check in
women who have no signs or symptoms for breast
cancer. It is called a screening mammogram.
Screening mammograms usually involve two x-ray
pictures. The tumors that cannot be felt can also be
detected. Screening mammograms find micro
calcifications which are tiny deposits of calcium that
sometimes indicate the presence of breast cancer.
A diagnostic mammogram is one which is used if a
lump or symptom of the disease has been found.
Pain, skin thickening, nipple discharge, or changes in
breast size or shape are the signs of the breast cancer.
But these signs may also be indicators of benign
conditions. Changes found during a Screening
mammogram are evaluated using a diagnostic
mammogram. The breast tissue which is difficult to
obtain in a screening mammogram can also be
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 47
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
viewed because of special circumstances, such as the
presence of breast implants.
3.2 Ultrasound
Ultrasound [5] is also called as sonography. It is an
imaging technique in which high-frequency sound
waves which cannot be heard by humans are bounced
off tissues and internal organs. The picture produced
by echos is called a sonogram. Ultrasound evaluates
lumps that are hard to see on a mammogram.
During an ultrasound examination, a thin coating of
lubricating jelly is spread over the area to be imaged
to improve conduction of the sound waves. The
transducer directs the sound waves through the skin
toward specific tissues. The sound waves are
reflected back from the tissues within the breast. And
the patterns formed by the waves create a twodimensional image of the breast on a computer.
Ultrasound cannot be used for routine breast cancer
screening because it cannot consistently detect
certain early signs of cancer such as micro
calcifications (tiny deposits of calcium in the breast
that cannot be felt but can be seen on a conventional
mammogram).
3.3 Computer-Aided Detection
Computer-aided detection (CAD) involves the use of
computers to bring suspicious areas on a
mammogram to the radiologist’s attention. The
radiologist does initial review of the mammogram.
And then CAD is done. An example is the Image
Checker. The mammogram is scanned with a laser
beam and converts it into a digital signal which is
processed by a computer. The image is displayed on
video monitor. The suspicious areas highlighted for
the radiologist to review. The radiologist compares
the digital image with the conventional mammogram
to check if any of the highlighted areas were missed
on the initial review and require further evaluation.
CAD technology improves the accuracy of screening
mammography. The incorporation of CAD to digital
mammography is under evaluation.
3.4 MRI
In magnetic resonance imaging (MRI), a magnet is
linked to a computer which creates detailed pictures
of areas inside the body without using the
radiation.MRI produces hundreds of images of the
breast which are taken from side-to-side, top-tobottom, and front-to-back. A radiologist interpretes
the images. Breast MRI is used for clinical trials
which are being performed to determine if MRI is
valuable for screening certain women, such as young
women at high risk for breast cancer. MRI cannot
always accurately distinguish between cancer and
benign (noncancerous) breast conditions. MRI
evaluates breast implants for leaks or ruptures, and
abnormal areas are assessed that are seen on a
mammogram or are felt after breast surgery or
radiation therapy. It is used after breast cancer is
diagnosed and determines the extent of the tumor in
the breast. MRI is also useful in imaging dense breast
tissue, which is mostly found in younger women, and
in viewing breast abnormalities that sometimes can
be felt but are not visible with conventional
mammography or ultrasound.
3.5 Breast tumour analysis in DCE-MRI using
Texture Features and Wavelet transform
We have taken the paper of NIC [17] as a reference.
In this paper the DCE_MRI images were used.
Intensity values at individual pixel lead to image
noise, motion artifact and MR artifact.They have
used texture features which are the most important
image attributes to identify and compare regions.
Then they applied wavelet transform to extract
frequency features from the temporal sequence. They
employed a progressive feature selection scheme and
a committee of support vector machines for the
classification. They trained the system on ten cases
and tested it on eight independent test cases.
Receiver-operating characteristics (ROC) analysis
showed that the texture temporal sequence is much
more effective than the intensity sequence. The
wavelet transform further improved the classification
performance. They considered that texture features
can alleviate the impact of artifact since it is based on
a large neighborhood and not pixel-to-pixel
correspondence and the texture features are much
more stable than the intensity alone, so they can be
used to address different image protocols much
better. The wavelet transform also reduces the
number of features. The 20-time-point signal
sequence is summarized in 12 wavelet features.
4. MEDICAL IMAGE ANALYSIS:
Image analysis techniques have played an important
role in several medical applications. In general, the
applications involve the automatic extraction of
features from the image which are then used for a
variety of classification tasks, such as distinguishing
normal tissue from abnormal tissue. Depending upon
the particular classification task, the extracted
features capture morphological properties, color
properties, or certain textural properties of the image.
The textural properties computed are closely related
to the application domain to be used. The
classification of pulmonary disease using texture
features. Some diseases, such as interstitial fibrosis,
affect the lungs in such a manner that the resulting
changes in the X-ray images are texture changes as
opposed to clearly delineated lesions. In such
applications, texture analysis methods are ideally
suited for these images. Sutton and Hall propose the
use of three types of texture features to distinguish
normal lungs from diseased lungs. These features are
computed based on an isotropic contrast measure, a
directional contrast measure, and a Fourier domain
energy sampling. In their classification experiments,
the best classification results were obtained using the
directional contrast measure.
Image texture in combination with color features is
used to diagnose leukemic malignancy in samples of
stained blood cells. They extracted texture microedges and “textons” between these micro-edges. The
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 48
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
textons were regions with almost uniform color. They
extracted a number of texture features from the
textons including the total number of pixels in the
textons which have a specific color, the mean texton
radius and texton size for each color and various
texton shape features. In combination with color, the
texture features significantly improved the correct
classification rate of blood cell types compared to
using only color features.
Extract we can various first-order statistics (such as
mean gray level in a region) as well as second-order
statistics (such as gray level co-occurrence matrices)
to differentiate different types of white blood cells.
We can use textural features in ultrasound images to
estimate tissue scattering parameters. They made
significant use of the knowledge about the physics of
the ultrasound imaging process and tissue
characteristics to design the texture model.
Use fractal texture features in combination with other
features (such as response to edge detector operators)
to analyze ultrasound images of the heart .The
ultrasound images in this study are time sequence
images of the left ventricle of the heart. Figure 7
shows one frame in such a sequence. Texture is
represented as an index at each pixel, being the local
fractal dimension within an window estimated
according to the fractal Brownian motion model . The
texture feature is used in addition to a number of
other traditional features, including the response to a
Kirsch edge operator, the gray level, and the result of
temporal operations. The fractal dimension is
expected to be higher on an average in blood than in
tissue due to the noise and backscatter characteristics
of the blood which is more disordered than that of
solid tissue.
4.1 Texture Feature Formula
a) Gray level co-occurrence matrices
A co-occurrence matrix [16] contains the frequency
of a certain pair of pixels repetition in an image. In
order to compute a co-occurrence matrix it is
necessary to know the following values:
i)Number of grey levels: a greyscale image contains
256 grey levels, which means a high computational
cost because all possible pixel pairs must be taken in
account. The solution is to generate the matrix
reducing the number of greyscales, and so the
number of possible pixel combinations. The cooccurrence matrix is always square with the same
dimensionality as the number of grey-levels chosen.
This value is often set to eight.
ii)Angle: Similarly to the distance it is necessary to
define the direction of the pair of pixels. The most
common directions are 0, 45º, 90º, 135º, and its
symmetric equivalents.
Distance between pixels: the co-occurrence matrix
stores the number of times that a certain pair of pixels
is found in the image. Normally the pair of pixels are
just neighbours, but it could also be computed the
matrix analysing the relation between non
consecutive pixels.
b) Histogram Statics
i) Mean
The mean of a data set is simply the arithmetic
average of the values in the set, obtained by summing
the values and dividing by the number of values.
Recall that when we summarize a data set in a
frequency distribution, we are approximating the data
set by "rounding" each value in a given class to the
class mark. With this in mind, it is natural to define
the mean of a frequency distribution by
The mean is a measure of the center of the
distribution. As you can see from the algebraic
formula, the mean is a weighted average of the
class marks, with the relative frequencies as the
weight factors. We can compare the distribution to a
mass distribution, by thinking of the class marks as
point masses on a wire (the x-axis) and the relative
frequencies as the masses of these points. In this
analogy, the mean is literally the center of mass--the
balance point of the wire.
ii) Variance and Standard Deviation
The variance of a data set is the arithmetic average
of the squared differences between the values and the
mean. Again, when we summarize a data set in a
frequency distribution, we are approximating the data
set by "rounding" each value in a given class to the
class mark. Thus, the variance of a frequency
distribution is given by
iii) Run Length Matrices [14]
For a run length matrix p(i; j), let M be the number of
gray levels and N be the maximum run length. The
four new matrices are defined as follows.
a)Gray Level Run-Length Pixel Number Matrix:
pp(I, j) = p(I, j) . j
Each element of the matrix represents the number of
pixels of run length j and gray-level i. Compared to
the original matrix, the new matrix gives equal
emphasis to all length of runs in an image.
b) Gray-Level Run-Number Vector:
N
Pg(i)=
j=1
This vector represents the sum distribution of the
number of runs with gray level i.
c) Run-Length Run-Number Vector:
M
i=1
This vector represents the sum distribution of the
number of runs with run length j.
5. RESULTS
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 49
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
We have used the database of DEC-MRI images. The
database contained about 200 images. We have used
these images for training and testing. We have first
extracted the features of some of these images. And
trained the neural network. And the remaining
images are used for testing.
Following table shows the values of intensity
of image, texture features and output of neural
networks after testing, and the number of datasets
used for these values.
No.
of
datasets
(%)
0.02
Intensity
Texture
NN
0
0
0
0.04
0.1
0.2
0.4
0.06
0.12
0.22
0.52
0.08
0.4
0.41
0.61
0.10
0.5
0.51
0.71
0.12
0.6
0.64
0.74
0.14
0.7
0.75
0.75
0.16
0.8
0.86
0.86
0.18
0.82
0.82
0.82
0.20
0.84
0.84
0.84
0.30
0.85
0.85
0.85
0.32
0.86
0.86
0.86
0.34
0.87
0.87
0.87
0.36
0.88
0.88
0.88
0.38
0.9
0.9
0.9
0.30
0.91
0.91
0.91
0.40
0.92
0.92
0.92
0.42
0.93
0.93
0.93
0.44
0.94
0.94
0.94
0.46
0.95
0.95
0.95
0.48
0.96
0.96
0.96
0.50
0.97
0.97
0.97
0.52
0.99
0.99
0.99
6. LIMITATION FUTURE SCOPE
This includes a complete and comprehensive study
of texture analysis and machine learning.
In breast tumour analysis using texture features and
wavelet transform ; the authors have studied 18
women suffering from breast cancer. Following are
the graphs of their results. An ROC curve is a plot of
sensitivity and specificity by varying over the
continuum of the decision threshold. The overall
performance of the classifier is expressed as a single
value of the area .They have plotted the pixel
intensity, texture sequence and wavelet features. The
intensity sequence generates many false positive
regions and does not highlight all the tumor regions.
The texture sequences are able to eliminate most of
the false positives. The wavelet features highlight
more cancerous regions.
Using these values we potted the approximate graph
of accuracy obtained in testing the images. Following
is the approximate graph.
Fig.1 No. Of datasets VS accuracy
In this vector machine for training and testing of
classification. In our project we have used the single
layer neural networks for machine learning. So we
have performed texture analysis, and used them for
training and testing. The advantage of this
modification is we can use more number of datasets
for training and testing purpose and still obtain
greater accuracy than support vector machines.The
following is the expected approximate graph which
shows the plot of number of datasets used and
accuracy of and training and testing by neural
networks. The accuracy of classification improves by
using NN than by using pixel intensity and texture
features.
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 50
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
COMPUTER ENGINEERING
This can be used as an utility by the
radiologists in cancer specialist hospitals. It provides
the most accurate and fast method of finding out the
stage of cancer of the suffering woman. In this thesis
we have found out the classification of the malignant
tumour. In future the same technique can be used for
finding out whether the tumour is benign or
malignant.
7. CONCLUSION:
This project is feasible for several reasons. First,
there is a lot of research that has been done in the
field of detection of cancer tumours, so we will be
able to refer pre-existing models.The project contains
several challenges that make it interesting to
develop.The main task is to find out the correct
region of interest i.e. the breast region separated from
chest region.Then use the various image attributes
that will provide the correct classification of that
image. And fast and efficient training of neural
network. Because of its user-friendly interface, the
application can also be used by anyone who wants to
test the image of breast cancer to find its stage. The
main advantage of this program for individuals is that
they do not have to have any medical knowledge
about the breast cancer.
8. REFERENCES:
[1]
“Statistical Texture Analysis”, G. N.
Srinivasan, and Shobha G., PROCEEDINGS OF
World ACADEMY OF SCIENCE ,ENGG. AND
TECH. VOLUME 36 DECEMBER 2008, ISSN
2070-3740
[2]
“Content Retrieval From X-RAY Images
Using Color & Texture Features”Bikesh Kr. Singh &
Bidyut
Mazumdar,International
Journal
of
Electronics Engineering, 2(1),2010, pp. 25-28
[3]
”Biomechanics & Biophysics of cancer
cells”,Subra Suresh,ScienceDirect April 2007
[4]
Breast Ultrasound Image Improvement by
Pixel Compounding of Compression Sequence Zhi
Yang, Sumedha P. Sinha, Rebecca C. Booi, Marilyn
A. Roubidoux, Bing Ma, J. Brian
Fowlkes,
IEEE Transactions on ltrasonics, Ferroelectrics, and
Frequency Control, vol. 56, no. 3, March 2009
[5]
. Breast Tumor Classification of Ultrasound
Images Using Wavelet-Based Channel Energy and
Image J Hsieh-Wei Lee, , IEEE JOURNAL OF
SELECTED TOPICS IN SIGNAL PROCESSING,
VOL. 3, NO. 1, FEBRUARY 2009
[6]
Breast Cancer: Modelling and Detection
D.J. GAVAGHANa, J.M. BRADYb, C.P.
BEHRENBRUCHb, R.P. HIGHNAMc and P.K.
MAINId, Journal of Theoretical Medicine, 2002 Vol.
4 (1), pp. 3–20
[7]
A 3D electrical impedance tomography
(EIT) system for breast cancer detection V
Cherepenin1, A Karpov2, A Korjenevsky1, V
Kornienko1, A Mazaletskaya3, D Mazourov4 and D
Meister5,
[8]
Proc Natl Acad Sci U S A. 2008 November
18; 105(46): 17937–17942. Published online 2008
November 13. doi: 10.1073/pnas.0804224105
[9]
International Journal of u- and e- Service,
Science and Technology Vol. 2, No. 3, September,
2009 Analysis and Diagnosis of Breast Cancer Poulami Das1, Debnath Bhattacharyya1, Samir K.
andyopadhyay2,and Tai-hoon Kim3
[10]
ROCK TEXTURE RETRIEVAL USING
GRAY LEVEL CO-OCCURRENCE MATRIX Mari
Partio, Bogdan Cramariuc, Moncef Gabbouj, and Ari
Visa Tampere University of Technology, P.O. Box
553.
[11]
Dynamic Neural Network Based Training
for Support Vector Machines Zeng-Guang Hou,
Madan M. Gupta, Fellow, IEEE Intelligent Systems
Research Laboratory Institute of Automation,
Chinese Academy of Sciences Beijing, 100080, RR.
China
[12]
Second International Multisymposium on
Computer and Computational Sciences SVM
Approach to Breast Cancer Classification Mihir
Sewak1, Chien-Chung Chan2, Zhong-Hui Duan2
[13]
Neural Network based Prediction of Protein
Structure and Function: Comparison with Other
Machine Learning Methods-M. Michael Gromiha,
Shandar Ahmad and Makiko Suwa
[14]
Texture Information in Run-Length Matrices
- Xiaoou Tang
[15]
Segmentation of the Breast Region in
Mammograms using a Rule-Based Fuzzy Reasoning
Algorithm ,M. Wirth, D. Nikitenko, J. Lyon ,
University of Guelph.ICGST-GVIP Journal, Volume
5, Issue2, Jan. 2005
[16]
ROCK TEXTURE RETRIEVAL USING
GRAY LEVEL CO-OCCURRENCE MATRIX Mari
Partio, Bogdan Cramariuc, Moncef Gabbouj, and Ari
Visa
[17]
IEEE JOURNAL OF SELECTED TOPICS
IN SIGNAL PROCESSING, VOL. 3, NO. 1,
FEBRUARY 2009Breast Tumor Analysis in
Dynamic Contrast Enhanced MRI Using Texture
Features and Wavelet Transform,Jianhua Yao,
Jeremy Chen, and Catherine Chow
[18]
Jianhua Yao, Jeremy Chen, and Catherine
Chow, and T. Tong. 2009, Breast Tumor Analysis in
Dynamic Contrast Enhanced MRI Using Texture
Features and Wavelet Transform, IEEE JOURNAL
OF
SELECTED
TOPICS
IN
SIGNAL
PROCESSING, VOL. 3, NO. 1, FEBRUARY,2009
[19]
J. Y. Lo, J. A. Baker, P. J. Komguth, J. D.
Iglehart, and C. E. Floyd.“Predicting Breast Cancer
Invasion with Artificial Neural Networks on the
Basis of Mammographic Features.” Radiology, vol.
203.
[20]
J. Y. Lo, J. A. Baker, P. J. Komguth, and C.
E. Floyd, Jr. “Effect of Patient History Data on the
Prediction of Breast Cancer from Mammographic
Findings with Artificial Neural Networks.”
AcademicRadiology, vol. 6, pp. 10-15, 1999.
ISSN: 0975 – 6760| NOV 10 TO OCT 11 | VOLUME – 01, ISSUE - 02
Page 51