Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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