CHAPTER 3 - DATA MING TECHNIQUES FOR MEDICAL IMAGE PROCESSING

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1 . CHAPTER 3 - DATA MING TECHNIQUES FOR MEDICAL IMAGE PROCESSING 3.1 INTRODUCTION The techniques of image processing are most important to understand and determine the symptoms of the physical nature, circulation of different part of the human body and its functional process. The Image Processing Techniques used to observe the abnormal functions and the information and communication computing technology has been played vital role in human life, includes in the field of medicine and health care. In determination of Medical diagnoses digital images of the human body help to understand the nature of human biological systems now days. These images may be at the molecular level or images of complete organs, organ systems, and body parts. These images are enormous sources of information and like any other source of information need to be tapped and analyzed to pave the way for better understanding disordered system to its internal and external level using modern observatory or radiology devices. Image Processing are used for various Medical biased applications. There are three primary application areas in this field: firstly, image restoration, secondly, the processing of data for autonomous machines perception and finally the processing of images for improvement in human perception, for example comparison or feature extraction. In medicine, image processing techniques have been used for assisting in diagnosis of syndrome and its stages with its nature and research. Various techniques for image improvement like image enhancement and image restoration are used. Image analysis techniques including 25

2 morphological image processing, edge detection, image feature extraction, image segmentation, shape analysis find much use in the medical field. Data mining techniques are used in the medical applications. The following part explains the data mining concepts. 3.2 DATA MINING TECHNIQUES The development of Information Technology has generated large amount of databases and huge data in various areas. The research in databases and information technology has given rise to an approach to store and manipulate this precious data for further decision making. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are used for knowledge discovery from databases Classification Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable 26

3 the rules can be applied to the new data tuples. For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-byrecord basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier. Types of classification models: Classification by decision tree induction Bayesian Classification Neural Networks Support Vector Machines (SVM) Classification Based on Associations Breast cancer in India is in rise and rapidly becoming the leading cancer in females and death toll is increasing at fast rate and no effective way to treat this disease yet [M.Vasantha et al 2010]. So early detection becomes a critical factor to cure the disease and improving the surviving rate. Generally the X-ray mammography is a valuable and most reliable method in early detection. Data mining of medical images is used to collect effective models, relations, rules, abnormalities and patterns from large volume of data. This procedure can accelerate the diagnosis process and decision-making. Different methods of data mining have been used to detect and classify anomalies in mammogram images such as wavelets [C.Chen et al 1997, T.Wang et al 1998],statistical methods and most of them used feature extracted using image processing techniques [S.Lai,X.Li et al 1989].Some other methods are based on fuzzy theory 27

4 [D.Brazokovic et al 1993] and neural networks [I.Christiyanni et al 2000].Most of the Computer Aided Methods proved to be the powerful tool that assists the radiologist to speed up the treatment process. The classification techniques are used to improve the accuracy of computer aided diagnosis through multimodality breast imaging.this research is used to evaluate the effect of using multiple modalities on the accuracy achieved by a computer-aided diagnosis system, designed for the detection of breast cancer. Towards this aim, 41 cases of breast cancer were selected, 18 of which were diagnosed as malignant and 23 as benign by an experienced physician. Each case included images acquired by means of two imaging modalities: x-ray and ultrasound (US). Manual segmentation was performed on every image in order to delineate and extract the regions of interest (ROIs) containing the breast tumors. Then 104 textural features were extracted; 52 from the x-ray images and 52 from the US images. A classification system was designed using the extracted features for every case. Firstly, features extracted from x-ray images alone were used to evaluate the system. The same task was performed for features extracted from US images alone. Lastly the combination of the two feature sets, mentioned afore, was used to evaluate the system. As a result of his research, proposed system that employed the Probabilistic Neural Network (PNN) classifier scored 78.05% in classification accuracy using only features from x-ray. While classification accuracy increased at 82.95% using only features from US, a significant increase in the system s accuracy (95.12%) was achieved by using combined features from both x-ray and US. In order to minimize total training time, the proposed system adopted the Client-Server model to distribute processing tasks in a group of computers interconnected via a local area 28

5 network. Depending on the number of clients employed, there was about a 4-fold reduction in training time employing 7 clients. The same techniques are used in Ultra sound image analysis for the same purpose. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification.[ H. D. Cheng,Juan Shan 2010, Lin Li et al 2011]. The same objectives are obtained using clustering process. The concept of clustering and the cluster based research work are presented Clustering Clustering can be said as identification of similar classes of objects. By using clustering techniques can further identify dense and sparse regions in object space and can discover overall distribution pattern and correlations among data attributes. Classification approach can also be used for effective means of distinguishing groups or classes of object but it becomes costly so clustering can be used as preprocessing approach for attribute subset selection and classification. For example, to form group of customers based on purchasing patterns, to categories genes with similar functionality. Types of clustering methods Partitioning Methods Hierarchical Agglomerative (divisive) methods Density based methods 29

6 Grid-based methods Model-based methods S.Saheb Basha proposed an improvement of early diagnostic techniques [K. Bovis et al 2002, S. Petroudi, et al 2003, H.P.Chan et al 2000, M.J. Bottema et al 2000] is critical for women s quality of life. Mammography is the main test used for screening and early diagnosis. Early detection performed on X-ray mammography is the key to improve breast cancer prognosis. In order to increase radiologist s diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary signatures of this disease: masses and micro calcifications. Masses are spaceoccupying lesions, described by their shapes, margins, and denseness properties. A benign neoplasm is smoothly marginated, whereas a malignancy is characterized by an indistinct border that becomes more speculated with time. Because of the slight differences in X-ray attenuation between masses and benign glandular tissue, they appear with low contrast and often very blurred. Micro calcifications are tiny deposits of calcium that appear as small bright spots in the mammogram. This paper presents a research on mammography images using Morphological operators and Fuzzy c means clustering for cancer tumor mass segmentation. The first step of the cancer signs detection should be a segmentation procedure able to distinguish masses and micro calcifications from background tissue using Morphological operators and finally fuzzy c- means clustering (FCM) algorithm has been implemented for intensity based segmentation [S.Saheb basha et al 2008]. Bhagwati [Bhagwati Charan Patel et al 2010] proposed an adaptive K-means segmentation method for detection of micro calcifications in digital mammograms. In that 30

7 work, they have made an attempt to improve the performance of existing K-means approach by varying various values of certain parameters discussed in the algorithm [T. Kanungo, et al 2002].The K-means algorithm is an iterative technique that is used to partition an image into K clusters. In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. To improve the accuracy and reliability of mass region segmentation, a large number of computing algorithms have been proposed, developed and tested, including multi-layer topographic region growth algorithms [Huo Z et al 1995, Zheng B et al 1995, Eltonsy NHet al 2007], active contour (snake) modeling [Lobreqt S and Viergever MA 1995], adaptive region growth [ Brake GM and Karssemeijer,2001], a radial gradient index (RGI)-based modeling [Yuan Y et al 2007], and a dynamic programming-based boundary tracing (DPBT) algorithm [Dominguez AR et al 2007]. Due to the diversity of breast masses and overlap of breast tissue in the 2-D projected images as well as the limited testing datasets, it is very difficult to compare the performance and robustness of these segmentation methods [Kupinski MA and Giger ML. 1998]. Features which are useful for characterizing lesions include their degree of spiculation, shape and texture [Lladó X et al 2009]. Spiculation features commonly involve the calculation of image gradient using, for example, the Sobel masks [Dougherty G, 2009]. The cumulative edge gradient, from the Sobel magnitude-of-edges image, can be plotted as a histogram of the radial angle, from the Sobel phase-of-edges image, to determine the degree of spiculation [Giger ML et al 1990, Huo Z et al 1998]. The FWHM (full width at half maximum) of the gradient is able to distinguish spiculated masses from smooth masses. 31

8 Others have used multi-scale oriented line detectors to detect and measure speculated masses [Parr T et al 1998]. The centres of mass lesions tend to be circular so that specific filters can be used [Kobatake et al 1996]. The boundary of the lesion can be unwrapped, and its difference from a smoothed version used to characterize the degree of speculation [Giger ML et al 1994]. Other relevant features include asymmetry, which would include automatic registration of left and right breast images [Yin FF et al 1994], and changes with time [Sallam M et al 1996]. Wavelets and Gabor filters have been extensively investigated and compared [Ortega M et al 1998], and Gabor filters have performed better and corresponded well to the human vision (in particular for the sensitivity of edge detection) [Daugman JG, 1993]. Other popular texture features derived from the co-occurrence matrices [Kuo et al WJ 2002] and Fourier transformation [Milanese R et al1999] have also been tested. Recently, fractal dimension has been shown to be an effective and efficient metric for assessing texture in the detection and classification of suspicious breast mass regions [Park SC et al 2009]. Fractal dimension can be used to distinguish between malignant and benign breast masses [Velanovich V.et al 1996], and has a high correlation with visual similarity [Chevallet JP et al 2006, Soares F et al 2007].In all above methods preprocessing complexity is involved Prediction Regression technique can be adapted for predication. Regression analysis can be used to model the relationship between one or more independent variables and dependent variables. In data mining independent variables are attributes already known and response variables are what we want to predict. Unfortunately, many real-world problems are not simply prediction. For instance, sales volumes, stock prices, and product failure rates are all very difficult to predict because they may depend on complex interactions of multiple 32

9 predictor variables. Therefore, more complex techniques (e.g., logistic regression, decision trees, or neural nets) may be necessary to forecast future values. The same model types can often be used for both regression and classification. For example, the CART (Classification and Regression Trees) decision tree algorithm can be used to build both classification trees (to classify categorical response variables) and regression trees (to forecast continuous response variables). Neural networks too can create both classification and regression models. Types of regression methods Linear Regression Multivariate Linear Regression Nonlinear Regression Logistic regression is a generalization of linear regression [E. D. Pisano et al 2007]. It is used primarily for predicting a dichotomous dependent variable or multi-class dependent variables. In producing the logistic regression equation, the maximum-likelihood ratio was used to determine the statistical significance of the variables. Logistic regression has proven to be very robust in a number of medical domains and is an effective way of estimating probabilities from dichotomous variables [W. Yang, 2006]. Logistic regression assumes that the response variable is linear in the coefficients of the predictor variables. Logistic regression analysis was performed with demographic and examinations related to breast cancer pathology variable as covariates, to assess the independent effect of each factor. [Fentiman IS, 1998] Breast cancer prediction is processed by Delen et al. [Delen. D et al 2005] using decision tree algorithm. Artificial neural network and logistics regression model 33

10 performance are overcome by Bellaachia et.al [Bellaachia et al 2006] using decision tree j48 algorithm. But these analyses are not directly implementable for the medical digital image analysis.the preprocessing is essential according to the selection of model and predictable techniques Association Rule Association and correlation is usually to find frequent item set findings among large data sets. This type of finding helps businesses to make certain decisions, such as catalogue design, cross marketing and customer shopping behavior analysis. Association Rule algorithms need to be able to generate rules with confidence values less than one. However the number of possible Association Rules for a given dataset is generally very large and a high proportion of the rules are usually of little (if any) value. Tripty et al [Tripty Singh et al 2010] carried his diagnostic work and implemented using data mining with expert system. The associative property of the affected level and the predictive procedure is derived. Further the mammographic views the mammogram and determines the presence or absence of image features such as calcifications and masses. Second, the presence and description of these features and the patient s medical history are merged to form a diagnosis. The case-based reasoning system is an aid to the second step and is motivated by the large fraction of biopsies that are benign. Mammography is a sensitive procedure for detecting breast cancer, but the positive predictive value is low. In his work, 10 34% of women, who undergo biopsy for mammographically suspicious impalpable lesions are actually found to have malignancy. Between 0.5% and 2.0%of all mammographic examinations result in biopsy; several hundreds of thousands of biopsies are performed on benign lesions each year. The women undergoing biopsy for a benign finding are 34

11 unnecessarily subjected to the discomfort, expense, potential complications, change in cosmetic appearance and anxiety that can accompany breast biopsy. The clinician interprets a mammogram and records the findings into a computer using a standard reporting lexicon (Breast Imaging Reporting And Data System [BI-RADS]) [T.Hopp, G.F.Schwarzenberg, M.Zapf, N.V.Ruiter]. The database is searched for similar cases, and the fraction of similar cases that were malignant is returned. This fraction is referred to as the malignancy fraction and is an intuitive response that the woman s health care team can then include in the medical decision for biopsy. The associative properties of biopsy and the cancer prevention is proposed as an expert system. In the digital mammographic analysis work is continued in Tamilnadu by Khaja Mohideen [K. Thangavel et al 2009] to detect the cancer using micro classifier techniques. The digital mammographic analysis process implemented using association rule mining. As a result, the shape features are extracted from the digital mammograms. With these feature values association rules are constructed to develop a rule based system for classification of microcalcifications. A novel Multidimensional Genetic Association Rule Miner (MGARM) is proposed for rule construction. The result shows that the proposed rule-based approach reaches the classification accuracy over 85% and also demonstrates the use and effectiveness of association rule mining in image classification. These shows that the data mining techniques such as clustering, classification predication and association rule mining are used for the mammographic analysis in the domain of medical image processing and analysis. All the research work are attempted to determine the mammographic level of analysis to identify the level of affected level of cancer and its causes otherwise the possibilities of prevention of cancer. The computational process, 35

12 the mining techniques classification used to segment the image of the mammogram. The segmented pixel or the digital data set are cluster as per the attribute and the research scope of the researcher as per their domain applications. The research work is identified that highly significant and required to increase the efficiency in the process of identification of breast cancer as presented in the previous chapter. The discussed techniques are adopted and discussed with suitability of this research under the discussion of Data Mining (DM) techniques for Digital mammographic Analysis (DMA) presented below. 3.3 DM TECHNIQUES FOR DMA As discussed and presented in from the introduction to till this chapter, digital mammographic is an essential application with social causes carried out different part of the work with various techniques which are discussed. The data mining techniques are identified potentially by the research community to identify the level of breast cancer using digital mammograms. The mammographic analysis adopted with computational techniques such as clustering, classification, predication and association. All the techniques and how they are approached and identified in this research is listed. Classification process is used to classify the selected Region of Interest Digital number into three layers according to the image property. These image property layers are namely Red, green and Blue. The range of these layers are from 0 to 255 contains 256 values. These values are used for the classification of five segment according to its average of five equal interval discrete value of each layer. Clustering process in implemented on the classified pixel values to bring the similar range values into a single cluster and form the cluster image to identify the properties. The similar 36

13 range values are evaluated in the pixel range and cluster into five cluster image. Each image pixel non- zero elements are processed to determine the correlation and density. Predication of the level of cancer depends of the affected area of the ROI and the density value. The individual density value and the correlation values are computed to predict the stage of the breast cancer. The confirmation process of the affected breast cancer and the level is ensured with the correlation values and occurrence value with the computation of confidence and support. Association is highly represents the dependency and the relationship between the density along with the level of stage of cancer. These associative relationship density indexes are computer with the average high occurrence value of density of pixel mode from the geometric preprocessed images. The preprocessing techniques and the implementation of above techniques are presented as a procedure in the next chapter. 3.4 DATA MINING TECHNIQUES FOR MAMMOGRAPHIC ANALYSIS These data mining methods that use physics-based mammogram representation [R. Highnam et al 1999, M. Linguraru et al 2001, M. Linguraru et al 2001a, M. Linguraru et al 2002], wavelet Transform [H. Yoshida et al 1994, H.Yoshida et al 1996, W. Zhang et al 1998, N. Wang et al 1998], machine learning algorithms [W. Zhang et al 1994, W. Zhang, et al 1996, S. Sehad et al 1997, T. Bhangale et al 2000, L. Cordella et al 2000, D. Edwards et al 2000, I. El-Naqa et al 2002], morphological filters [D. Zhao et al 1992], multi resolution analysis [T. Netsch et al 1999] and fuzzy logic [N. Pandey et al 2000]. For preprocessing mammogram images, local contrast enhancement [A. Laine et al 1994, W. Veldkamp et al 2000], noise equalization [K. McLoughlin et al 2004] and tissue thickness correction [P. Snoeren et al 2004] have been used. Finally, classification algorithms have been applied to 37

14 identification of micro classification that, with certain probability, indicates malignant process within a breast [Y. Jiang et al 1996, W. Veldkamp et al 2000]. The vast amount of research related to analysis of digital mammograms, as well as widespread interest from the medical community, stimulated the development of commercial computer-aided detection systems [S. Astley, F. Gilbert, 2004]. All the above mentioned approaches are required the high complex level of computation which required the more memory and the computational complexity. The analysis required the image to process and identify the mammographic analysis. The image analysis techniques and the computation procedures are carried out the major preprocessing of the data analysis and the preprocessing are unique. The image analysis required special hardware, high processing system and memory but the current approach is required simple number based classification with the minimal data set analysis approach using macro array construction for the Region of interest and multi attribute and common attribute evaluation process. 3.5 APPROACH AND ANALYSIS This determination of cancer and the level of cancer accuracy and the simple computational approach is achievable using the data mining association rule model. This model will reflect the affected cells level of density. This approach is adopted from the above discussed research specifications. This model is used to specify the conversion of images into the corresponding digital numbers which reflects the value of the emission of the cells of the body. The images reflect the existing nature of the tissues. The affected cells and the level of affection of the surface will be aid to determine the level of cancer. This determination process will associated the level of density and the stage of the cancer. The cancer level or 38

15 stage of the cancer is the amount of affected tissue and the possibilities to cure the affected tissues. Therefore, the reflection of the stage and the level is directly proportionate to the pixel values. This approach is attempted to identify the affected cell and its corresponding total values. This total value is measure to determine the density ratio. Based on the density ratio of the affected cells the cancer level is attempted to determine.in this process, the level of affection are mapped with the selection of analysis images. The analysis processes ensure the result via processing common attribute between the images and the multiple attribute of the pre processing techniques. The approach is designed and implemented and discussed in the fourth coming chapter. 3.6 SUMMARY This chapter provides the overview data mining techniques and its functionalities for the predication and description of the data process. The data mining techniques such as clustering, classification, predication and association rules and its digital mammographic analysis work for the breast cancer detections system is evaluated along with the research procedures. The identification of the mining functionalities, the classification identified for the image segmentation for the ROI, clustering used for the pixel merging, predication is used for the density analysis and the association rule proposed for the mapping of level of cancer with density index. The above identified approach is converted and implemented in this research work is explained in the next chapter under multi view univariate classification algorithm. 39