International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
Computer Aided Detection Algorithm for Digital Mammogram Images – A Survey Bommeswari Barathi1, Siva Kumar.R2, Karnan.M3 1
P.G Scholar, Computer and Communication Engineering, Tamilnadu College of Engineering, Coimbatore, India. 2 3
Professor, Dept. of Information Technology, Tamilnadu College of Engineering, Coimbatore, India.
Professor and Head. Dept. of Computer Science and Engineering, Tamilnadu College of Engineering, Coimbatore, India.
ABSTRACT: In worldwide, Breast cancer is one
2. Mammography
of the leading disease among the women, under the age group of 15- 54. An automatic detection of micro calcifications is performed in magnetic resonance imaging system. Here discuss about, preprocessing and enhancement, feature extraction, segmentation, classification and analysis steps in the stage of preprocessing and enhancement, the medical MR images are enhanced by the computer aided detection algorithm. Segmentation performed by using K means clustering algorithm and then feature extracted by gray scale co-occurrence matrix (GLCM). Classification is done by support vector machine. Finally analysis determines using receiver operating characteristics.
Mammography is high-resolution x-ray imaging of the compressed breast which involves radiation t r a n s mi s si o n t h rou gh t h e tissue and the projection of anatomical structures on a film screen or image sensor. Though the x-ray imaging projection is a reduction in anatomical information from a 3D organ to a 2D film/image. Hence the Two imaging projections of each breast, cranio caudal (CC) and medio lateral oblique (MLO) are routinely obt ain ed which indicates three dimensions to understand the overlapping structures. High quality mammogram along with high spatial resolution through the adequate contrast separation allows the radio logiest for observing the fine structure. Thus the both method shows that mortality rate decrease by 30% of all women age 50 and older has regular mammograms. Breast cancer usually appears by distributed ducal structures. Breast cancer consists of three major types. They are 1.Circumscribed/oval masses, 2. Spiculated lesions and 3.Microcalcification.The above three types deals with malignant and benign lesions. Malignant lesions consist of more irregular shape than the benign lesions. Circumscribed masses deal in the form of compact and roughly elliptical. Spiculated lesions consist of central tumor mass and it is surrounded by radiating pattern of linear spicules. Micro calcifications visible as bright dot spots are the form of clusters. These represented as calcium deposits from call recreation and necrotic cellular debris. Two types of micro-calcification are, 1. Benign microcalcifications have the features of high uniform density with smooth and sharply outlined. 2. Malignant micro-calcification visible as irregular shape and distributed variably.
Keywords-Magnetic resonance image (MRI), pre processing and enhancement, feature extraction, classification.
segmentation,
I. INTRODUCTION 1. Breast Cancer Breast cancer is one of the most common disease among women and that lead to causes death under the age group of 15-54.In 1996, the American cancer society estimated that 184,300 are diagnosed by breast cancer, in that around 44,300 are women, Where as another study showed that approximately 720,000 new cases will be diagnosed per year which results about 20% of all malignant tumor cases. The world health organization’s international agency for research on breast cancer estimates more than 150,000 women die due to breast cancer each year in world wide. Since breast cancer is leading disease in world detection at the right time is crucial. The early detection will leads to the chance of survival. Screening mammography is only method that available in current for early and potentially curable for breast cancer.
ISSN: 2231-2803
II. DETECTION To automatically detect the breast cancer through MRI, here introduced the computer aided diagnosis (CAD) system. In computers aided detection, the receiver operating characteristic
http://www.ijcttjournal.org
Page138
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014 (ROC) is used to evaluate its performance in the system and resulted in description of detection or diagnostic approximately. In computer aided detection, there are two classes are available. They are, 1) One class is either cancer non abnormal class (True positive) and 2) The other class is normal class (True negative). Table 1- An Overview of Detection Techniques Methods Remarks Back propagation neural Used to detect the network[14] micro calcification clusters and show the result of 84.3% Position emission To detect the breast mammography Camera cancer is the earlier [39] stage. Nuclear magnetic moment To point the random vectors [18] directions of the atoms and align them direction to the direction of magnetic field. Forward Fourier To find the spacing transform and inverse between the object forward transform.[8] and the image. The shows the overlap with the results to each image contain zero is normal. Micro-calcification Helps to improve the algorithm, wavelet diagnostic accuracy transform, fuzzy shell and enhances the clustering [15] quality of mammograms using multi reduction analysis and detect through the introduction of shape information and thus it helps to canyon with the experimental results to confirm the activity. Region of interest Used to identify the identification fuzzy shell region that encircles clustering nodular with the microextraction. [4] calcification clusters and helps to reduce the iteration by analysis the detection between the valid microcalcification region and invalid one. Bayesian network by To find the breast directed acyclic graph cancer at the early [20] stage and helps to
ISSN: 2231-2803
Plasmon coupling effects [23]
Computer aided diagnosis schemes. [27]
take order into an account and find their dependencies relation is the single model. Hence obtained the results are improved by the approaches results. To identify the breast cancer biomarkers by colour changes and then generate to analysis by using dark field microscope. Used to develop the detection of primary signatures of these diseases along with masses and microcalcification.
III. DATABASE (IMAGE ACQUISITION) All the mammograms are used to obtain by the digital database for screening mammography (DDSM). The DDSM database comprised of digitized mammograms by associated ground truth and other information. The main purpose of database used for providing a large set of mammograms that are free, which can also be used by researchers to evaluate along with the comparison of performance in the computer aided detection (CAD) Algorithm. The database contains 2620 cases available along with 43 volumes of each case having four views, (medio lateral, oblique and cranio caudal views of left and right breasts).
IV. PREPROCESSING AND ENHANCEMENT The stage of preprocessing and enhancement is the simplest model of medical image processing. This process helps to reduce the noise and improves the quality of digital images.
1. Preprocessing Preprocessing helps to identify different scale of signal intensities of different images. Preprocessing function involve the operation to analysis the data and extract the information the preprocessing techniques such as adaptive filter, histogram equalization, weighted K mean clustering are compared. Table 2 - An Overview of Preprocessing Techniques Methods Remarks Fuzzy c means [32] To remove the diffusion. Contrast enhanced Capacity to correlate the
http://www.ijcttjournal.org
Page139
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014 MRI [11]
Computer aided detection Algorithm [17]
Computer image processing techniques [42] Adaptive filter, histogram equalization, histogram modified local contrast enhancement [34] Gaussian smoothing with median filtering [9]
features is mammograms by enhancing the regions and Used to classify the 3D distribution of micro-calcification structures and also correlates to the enhancement characteristics of ducts. Used to collect the resources to provide large set of mammograms is the digital format Used to enhance the images and leads to segmentation by using the region of interest. Used to improve the quality of images by removing the noise and then enhances by adjusting the value of parameter Used to enhance the images with high quality of demonising
algorithm wavelet transform, fuzzy shell clustering [5]
Gray scale histogram equalization, wavelet transform, digital database screening mammograms [7]
Region of interest identification, fuzzy shell clustering nodular extraction [8]
2. Enhancement Enhancement methods used to improve the visual appearance of images from magnetic resources (CT) scan, positron emission tomography (PET) and contrast enhancing based on height of “interesting” tissue such as fibrous or glandular tissues and cancerous tissues, where it allows for more accurate analysis of the mammograms. Enhancement techniques like histogram modified local contrast enhancement, local range modification and discrete wavelet are used. Table 3 - An Overview of Enhancement Techniques Methods Remarks K mean and fuzzy c To improve the mean algorithm [32] performance in noisy images and also to result in terms of speed, robustness and accuracy. Conceptual geometry Helps to biased to projection process [11] voxels with high enhancement to produce the projection indemnity Pharmacokinetic model Used to chosen the [4] actual enhancement characteristics Micro calcification Helps to improve the
ISSN: 2231-2803
Histogram modification [48]
Haar wavelet transform [28]
http://www.ijcttjournal.org
diagnostic accuracy and enhances the quality of mammograms using multi reduction analysis and detect through the introduction of shape information and thus it helps to canyon with the experimental results to confirm the activity Used to construct the images by representing the indemnity values of pixels based on the image histogram and modifies with visual artifacts and then decompose the images with high level of reductions Used to identify the region that encircles with the micro calcification clusters and helps to reduce the iteration by analysis the detection between the valid micro calcification region and invalid one. Thus among images 95% micro calcification are detected correctly and 5% were failed to find, though they are not nodular in structure. Therefore concluded the better way to detect micro calcification without nodular structure. To control over the level of enhancement that deals with the adjustment of features. Hence the work extended to test the presence of mammogram images in the presence of noise and can be employed as a pre processing step for analysis of micro calcification in mammograms Helps to the accelerate the signals of damaged Page140
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
Computer assisted diagnosis software [16]
Histogram based method, effective multi peak generalised histogram equalisation [3] Local range modification redundant discrete wavelet linear stretching and shrinkage algorithm [10] Incremental fuzzy mining techniques [37]
Particle swarm optimization [25]
Markov random field [35]
Anisotropic diffusion and weighted k means clustering [30] Pixel intensity transformation, spatial filtering, log arithmetic transformation, local and global threshold techniques , contrast stretching, histogram equalization [15]
primitive feature using wavelet co efficient for single degree of freedom system. Used to examine the mammograms. Hence helps to detect the small calcium deposits and there by enhances the quality of image with high reductions Used to segment the image and compose with the original images and helps locate the peaks. Used to visible the small calcium deposits
the features from the images data, through which a description, interpretation or understanding the features. The images can be segmented based on the intensity, region, and threshold values. The usage of K- mean algorithm is simplest elegant segmentation method and it deals with the unsupervised clustering algorithm that used to classifies the input data points into multiple classes depended on their distances. Thus the high pass sharpened images are segmented to provide thresholds the image in four of the common methods used to perform the regimentation methods are,1) Fuzzy logic, 2) Expectation Algorithm, 3) Vector quantization,4) K means clustering, 5) Matching property. It is issued to segment the image and then proceed to detect the part, thereby helps to collect information from the image and to achieve the segmentation method. Table 4 - An Overview of Segmentation Techniques
Used to solve the complexities among the higher dimensional noisy data. Used to solve the problem influenced by the dimension and enhances the swarm size, neighbourhood, acceleration co efficient and random values Used to filter the noise and enhance the iteration through the clustering process and thus result with α=0.5 on the noise. Enhancing the contrast among the images by reducing the noise. Used to segment the transformation among the MR images and extract the features by filtering from unwanted signal noise and distortion
Ant colony optimization algorithm [8]
Vector quantization, k mean clustering, matching property [27][6] Gray level co occurrence matrix, water shed algorithm kekre’s median codebook generation algorithm [17] Fuzzy c mean segmentation methods, kernel function based on FCM methods [16] Computer image processing techniques [42]
V. SEGMENTATION In the stage of segmentation, the images are partition or separated into region of similar attribute or characteristics. The main aim to extract
ISSN: 2231-2803
Methods Functional MRI,cardiac MRI, magnetic resonance angiography[32] Computational intelligence [9] Fuzzy logic, expectation maximization algorithm [3]
Region based methods, edge based methods [3]
http://www.ijcttjournal.org
Remarks Used to segment the images that can be used is various diagnostics Efficiently used for segmentation To collect information from the image and to achieve the segmentation method. To solve the optimization problem and segment the MR images. Used to segment the images and then proceed to the detection part. Used to segment the image into the equal exact size and then generate the desired size of the resulted rate is 68.5%. Used to segment the breast regions by using breast MRI images and helps to cover the entire images. Used to enhance the images and leads to segmentation by using the regions of interest. Used to separate the region by locating the boundaries from Page141
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
Laplacian kernel method [2] Image processing threshold, edge based and watershed algorithm [33] K mean clustering [6] Threshold techniques, hybrid techniques [34] Fuzzy c means along with feature extraction techniques and texture based segmentation [37] Bayesian maximum likelihood classifier [21]
Granulometry normalized size destruction [35] Wavelet transformation and k mean clustering using intensity based segmentation and expectation maximum algorithm [45] Soft computing approaches using edge based techniques [43] Multiphase level set approach [38]
Fuzzy c mean clustering algorithm generalized with objective functions least squares [11]
Ward’s clustering methods, fuzzy k mean clustering with membership function
different regions and also grouping by individual pixels. Helps to locate the edges through regions. Used to segment the mammogram breast cancer image based on time consuming and simplicity Used to partition the cluster Used to postulate all pixel values that lies in the certain range. Used to segment into three features such as entry, standards deviation and number of pixels. Used to slice the regions and helps to interactive with thresholding values and leads to the membership value less than the threshold value of 0.8. Used to pattern the spectrum in the MR images Used to divided based on the intensity value and helps to sharpen the images and validates to detect tumour region from an MRI images Process of partitioning takes place and among the multiple regions or set of pixels. Used to minimize the set of functions towards the desired image features like object boundaries Used to segment the aggregate clusters by using three norms such as diagonal, mahalonobis, Euclidean and adjusting the weighted factor. Clusters segments small sets of compound to highlight and identify the multi
ISSN: 2231-2803
[24]
Pixel intensity transformation, spatial filtering, log arithmetic transformation, local and global threshold techniques, contrast stretching, histogram equalization [15] Kekre’s median code book generation and kekre’s fast code book generation [13]
cluster membership along with the outlier compounds. Used to segment the transformation among the MR images and extract the features by filtering from unwanted signal, noise and disortation. Used to segment only the proper tumour spot in the MR images.
VI. FEATURE EXTRACTION Feature extraction is main important step in which ultimate the performance of the system is determined by optimal parameter of the classifier, to intrinsic separability of the feature vectors. The attributes of the normal poses the real challenges due to the complexity of normal tissues fact in the normal mammograms is not well defined. Hence breast cancer cannot be easily distinguished through the surrounding of normal tissues, only the heterogeneous nature of different breast cancer of different size poses the real challenges to extract the features. Thus the techniques that are used to separate the normal and abnormal region by using. 1) Curvilinear features, 2) Gabor features, 3) Gray level co-occurrences features. Table 5 - An Overview of Feature Extraction Techniques Methods Remarks Wavelet decomposition To extract the Gabor and artificial neural feature from the networks[14] original image(region of interest) and shows the result of 84.3% Conceptual geometry Helps to map to the projection process[11] intensity plane. Contrast enhanced Capacity to correlate MRI[1] the feature is mammograms by enhancing the regions. Region of interest Used to identify the identification. Fuzzy region that encircles shell clustering nodular with the micro extraction.[5] calcification clusters and helps to reduce the iteration by analysis the detection between the valid micro calcification region and invalid one. Thus among images 95% of micro calcification are
http://www.ijcttjournal.org
Page142
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
Haar wavelet transform.[28]
Gray level co occurrence matrix, water shed algorithm kekre’s median codebook generation algorithm. [27] Genetic algorithm, ant colony optimization algorithm, special gray level dependence method [26] Contourlet transform with laplacian pyramidal filter bank and steerable Gaussian filter.[2]
Haralick’s feature with gray land co occurrence matrix.[14]
Gabor and haralick features along with the gray level cooccurrence matrix.[9]
Correlative feature analysis along with full field digital
detected correctly and 5% were failed to find, through they are not nodular in structure. Therefore concluded the better way to detect micro calcification. Here, from the damage sensitive feature, the vibration of signal is derived and shows that energies of wavelet coefficients at the accurate scales can be referred as damaged features. Used to extract the needed size of images that have to classify and analysis. Hence the resulted rate is 68.5%. Used to select the feature and perform the comparison and then extraction is done. Helps to extract the images and to identify the distortion and used to detect 1502 regions of interests of 247 true positives and 1255 false positives. Hence results are obtained as 0.77 with 26 textural features. Helps to produce the changes in the intensity values of histogram as the function of distance and direction. Used to extract the features for developing the image signature over the measurement of similarity and thus results in averaging the precision value between o.5 and 0.61 using haralick features, 0.49 and 0.57 using Gabor features and o.51 and 0.78 using combination of Gabor and haralick features. Used to extract the features from the segment lesions.
ISSN: 2231-2803
mammography and dynamic contrast enhanced magnetic resonance imaging [22] Bio-inspired algorithm[50] Finite Gaussian mixture model[36]
Used to extract the feature by solving the optimization problems. Used to get the external parameter and threshold valued based on the segmented images that yields better results.
VII. CLASSIFICATION Classification method is used to separate the image to get the result of normal or abnormal regions. After segmentation of the mammogram images and classify them as benign, malignant or normal, thereby helps to predict the texture features which plays a vital role for classification. The size and stages of the cancer is detected and calculated. The classification techniques are used as follows. 1) Decision tree, 2) Support vector machine, 3) Neural Networks, 4) Bayesian learning are discussed. Table 6 - An Overview of Classification Techniques Methods Remarks Back propagation Used to classify the neural network[14] features that one extract as malignant being or normal and shows the result of 84.3% Principles of To solve the pattern component analysis, recognition problems support vector machines[32] K nearest Used in non parametric neighbours[18] methods to find the specific image from the density model. This shows the overlap with the results to each image contain zero (that is normal) or more lesions (that is abnormal) Contrast enhance Used to classify the 3D MRI[11] distribution of micro calcification structures and also correlates to the enhancement characteristics of ducal carcinoma. Back propagation Selected feature are fed network hybrid with to the layer of neural to act colony perform classification
http://www.ijcttjournal.org
Page143
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014 optimization [26] Clustering methods[16]
Artificial neural network with roughness measures, haralick’s measures, law’s measures[2] Seeded region growing, split and merge techniques[34]
Dual three complex wavelet transform pyramid structured wavelet transform, quad tree decomposition , current transform, discrete wavelet transform[3] Support vector machine[49]
Computer aided diagnosis, linear support vector machines, area under the receiver operating characteristic curve[29] Artificial neural network, genetic algorithm, decision tree, fuzzy c mean[21]
Decision tree algorithm with quinlan’s ID3 C4.5,C5[37]
Used to divides the cluster and helps to compute the average of the pixels. Used to classify the architectural disortation AD0 and non AD images. Used to split the images to satisfy the homogeneity criterion and postulate the similarity of pixels among the regions Used to subsets multidimensional data accuracy and then maps the best set of clusters into 2D for visualization
Classification and regression tree algorithm[40]
Bayesian artificial neural network[22]
Modified fuzzy c mean classification algorithm[35] Conventional fuzzy k mean clustering algorithm [10]
cases and test data as 0.9878 for 246 cases. Uses the historical data to construct the decision tree which helps to classify the information as data set to get new observations Depending on the probability of correspondence values the features sets are divided into subsets. Used to divide the fuzzy partition Helps to classify the cluster centres as active and stable groups and resulting calculating by 38.9% to 86.5% using the same data sets.
VIII. ANALYSIS To solve the problem of linearly separable, binary classification problem Classified unimodal malignancy to estimate the multimodal by averaging and hence successfully resulted is classification and comprising 39% of original feature sets. Each algorithm are used to classify accurately the positive predictive values and used to evaluate the data analysis. Thus result with appropriate where fuzzy c mean was 0.953, decision tree model were 0.9634, artificial neural network were 0.96502, genetic algorithm model were sensivities 0.9878. Used to select the variable accounting to the specific criterion such as information gain, gini index and chi squared test. Thus results with fitness value of 1, accuracy are train data as 0.993 for 453
ISSN: 2231-2803
The purpose of analysis stage is used to create the methods to get the accurate results. Hence analysis can be computed by building software for MR image. The techniques that are used to analysis are, 1) Region of interest - Which used to compute feature values with the regions and helps to convert the text format into digital format and 2)Feature maps – Through which are masked by using non linear filtration. Table 7 - An Overview of Analysis Techniques Methods Remarks Computer aided Used to process and diagnostics[18][51] analyse the volume of images to collect the high quality information and whether the disease is diagnose and treatment starts for the patients. Thus shows the overlap with the results to each image certain zero (that is normal) or more lesions (that is abnormal). Fourier theory[18] To find out the shaped area of the image. This shows the overlap with the results to each image contain zero (that is normal) or more
http://www.ijcttjournal.org
Page144
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
Pharmacokinetic model[11]
Histogram modified contrast limited adaptive histogram equalization [48]
Histogram modification[10]
Haar wavelet transform[28]
Back propagation network hybrid with ant colony optimization[26] Automated system[42]
Morphological filter using top hat transform[2] Fuzzy c mean algorithm[33][52]
lesions (that is abnormal). Used to chosen the actual voxel enhancement characteristics and also map the projection intensities for each voxel. to determine the parameter like enhancement measures and thus result to provide the better contrast enhancement with preservation of local information in the mammogram images. To control over the level of enhancement that deals with the adjustment of features. Hence the work extended to test the presence of mammogram images in the presence of noise and can be employed as a preprocessing step for analysis of micro calcification in mammograms. From the extraction at the higher scales are functioned with the physical parameter to analysis the image along with the vibration of vector. To evaluate the performance of feature selection with the classified results. By extract from ROI which helps to analysis the digital mammography. Used to analysis through processing of geometrical structures. Used to perform pattern recognition and then helps to analysis the data between two or more clusters.
ISSN: 2231-2803
Gabor wavelet with Gabor filter karhyunenloeve transform otsu’s method[34]
Genetic algorithm[21]
Biologically inspired algorithm with swarm filter[19]
Computer aided diagnosis (CADx) along with correlative feature analysis (CFA)[20] Moving particle semi implicit method (MPS)[41] Image histogram, co occurrence and run length matrices, image gradient, auto regressive model and wavelet transform[48] Geometric analysis[50]
Mathematical morphology[12]
Clustering algorithm[47]
CAD approach including fuzzy logic, neural network and hybrid algorithm[44][52] Enhanced artificial bee colony optimization algorithm.[46]
http://www.ijcttjournal.org
Used to extract the elementary function from the input features and helps to analysis the different scales and orientation. Used for detecting and diagnosing micro calcification pattern in digital mammography. Used to focus on the behaviour collects the require task with physical derived or adhoc-model. Then start analysis by using swarm hunting on the system performing into gray scale values and represent the output signal. Used to analysis and result in improved diagnostic accuracy. Used to analysis the deformation tissues in the breast. Used to calculate the features and helps to analysis the image through textures
Used to convert 2D to 3D model and then analyze the images. Performance of analysis takes place among the pixels along with the neighbours Analyzed based on their clustering efficiency Used to analyze the pattern recognition for the medical images. Used to identify the suspected regions based on bilateral subtraction between the left and right breast images.
Page145
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014 [12]
IX. CONCLUSION In this survey paper, method of detecting breast cancer by using magnetic resonance imaging has been studied and discussed. This helps for focusing the future develops in the field of medical image processing. Here detailed explanation about several techniques of medical image processing in breast cancer detection. This paper mainly deals with the information about the various stages of detection such as (1)pre-processing and enhancement technique, (2) segmentation(3) feature extraction, (4) classification, (5) Analysis are studied and explained.
REFERENCES [1]
[14]
[15]
[16]
[17]
Ali Qusay Al-Faris, Umi Kalthum Ngah, Nor Ashidi Mat Isa, Ibrahim Lutfi Shuaib, “Breast MRI Tumor Segmentation using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering”, Imaging and Computational Intelligence Research Group (ICI), Universities Sains Malaysia, 2012. [2] Anand S , Aynesh Vijaya Rathna R,”Architectural Distortion Detection in Mammogram using Contourlet Transform and Texture Features”, International Journal of Computer Applications (0975 – 8887) Volume 74– No.5, July 2013. [3] Andrzej Materka, “Magnetic Resonance Image analysis through soft-computing: Visualization Segmentation and diagnostic tool for Magnetic Resonance images”, Developed Within the Framework of The European COST (Cooperation in the Field of Scientific and Technical Research), 2004. [4] Angayarkanni, Pitchumani S, Kamal, Nadira Banu, “MRI Mammogram Image Classification Using ID3 Algorithm”, Published in Image Processing,2012. [5] Balakumaran T, Vennila, Gowri Shankar C,“Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering”, International Journal of Computer Science and Information Security, Vol. 7, No. 1, 2010. [6] Bhagwati Charan Patel, Sinha.G R, “An Adaptive Kmeans Clustering Algorithm for Breast Image Segmentation”, International Journal of Computer Applications (0975 –8887) Volume 10 no.4, November 2010. [7] Bharati R Jipkate, Gohokar V V, “A Comparative Analysis of Fuzzy C-Means clustering”, international Journal of Advanced Science and Technology Vol. 19, June, 2008. [8] Chattopadhyay S, Pratihar D K, De Sarkar S C, “A Comparative Study Of Fuzzy C-Means Algorithm And Entropy-Based Fuzzy Clustering Algorithms” published in Computing and Informatics, Vol. 30, pp 701–720,2011. [9] Chikamai K, Viriri S, Tapamo J R, “Combining Feature Methods for Content-Based Classification of Mammogram Images”, International journal of computer and communication, ISSN 1841-9836 8(4):499-513, August 2013. [10] Chit-tang chang, Jim Z C Lai, Mu-derjeng, “A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement”, journal of information science and engineering 27, 995-1009, 2011. [11] Christian P Behrenbruch, Kostas Marias, Paul A Armitage, Michael Brady J, Jane Clarke, Niall Moore, “The Generation of Simulated Mammograms from Contrast Enhanced MRI for Surgical Planning and Postoperative Assessment”, Magnetic Resonance Imaging Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK, 2000.
ISSN: 2231-2803
[13]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
Christina Olsen, “Image Analysis Mathematical Morphology”, Department of Computing Science Umea University, Feb 2009. Clifton C, vaidya J, “K Means Clustering Algorithms”, International Journal Computational Engineering Research / ISSN: 2250–3005, 2012. Dheeba J, Wiselin Jiji G “Detection of Microcalcification Clusters in Mammograms using Neural Network”, international Journal of Advanced Science and Technology Vol. 19, June 2010. Gandhi K R, Karnan M, “Mammogram image enhancement and Segmentation”, Computational Intelligence and Computing Research (ICCIC), 2010. Greeshma Gopal, Grace Mary Kanaga E, “A Study on Enhancement Techniques for Mammogram Images”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 2, Issue 1, January 2013. Hongmei Zhu, “Medical Image Processing Overview”, University of Calgary, 2003. Horia Mihail H Teodorescu and David J Malan, “Swarm Filtering Procedure and Application to MRI Mammography”, Harvard University, USA, Manuscript accepted for publication, July 2010. Jadid Abdulkadir, Suet-Peng Yong, Oi Mean Foong, “Variants of Particle Swarm Optimization in Enhancing Artificial Neural Networks”, Australian Journal of Basic and Applied Sciences, 7(9): 388-400, ISSN 1991-8178, 2013. Jiyun Park, Jong-Souk Yeo, “Nanoplasmonic-based Colorimetric Detection of MicroRNA miR-21 in Breast Cancer Cells”, Biomarkers, Vol. 13, No.7-8, 2008. John D Holliday, Sarah L Rodgers, Peter Willett, “Clustering Files of Chemical Structures Using the Fuzzy k-Means Clustering Method”, Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K, November 2003. Kaabouch N, Wen Chen Hu, “A Survey Of Medical Imaging Techniques used For Breast Cancer Detection”, Published In Electro/Information Technology (EIT), 2013. Karnan M, Siva Kumar R, Almelumangai M, Selvanayagi K and Logeswari T, “Hybrid Particle Swarm Optimization for Automatically Detect the Breast Border and Nipple position to Identify the Suspicious Regions on Digital Mammograms Based on Asymmetries”, International Journal of Soft Computing 3 (3): 220223,2008. Karnan M, Thangavel K , “Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications”, Computer methods and programs in biomedicine 87 (1), 12-20, 2007. Karnan M, Thangavel K, Siva Kumar R, Geetha K, “Ant colony Optimization for Feature Selection and Classification of Microcalcifications in Digital Mammograms”, International Conference on Advanced Computing and Communications,2006. Karnan M, Thangavel K , Siva Kumar R ,Geetha K , “Ant colony Optimization for Feature Selection and Classification of Microcalcifications in Digital Mammograms”, Advanced Computing and Communications, 2006. Kekre H B, Tanuja Sarode, Saylee Gharge, Kavita Raut, “Detection of Cancer Using Vector Quantization for Segmentation”, International Journal of Computer Applications (0975 – 8887) Volume 4– No.9, August 2010. Krishnan Nair K, John A Blume , Anne S Kiremidjian, “Derivation of a Damage Sensitive Feature Using the Haar Wavelet Transform”, International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA/CRC, New York,2010. Lesniak J M, Van Schie G, Tanner.C, Plate B, Huisman H, Karssemeijer N, “Multimodal Classification of Breast
http://www.ijcttjournal.org
Page146
International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 3– Feb 2014
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
Masses in Mammography and MRI Using Unimodal Feature Selection and Decision Fusion”, Radboud University Nijmegen Medical Centre, Department of Radiology, The Netherlands, 2007. Manojkumar S, Vilas Thakare, “MRI Image Processing with Intelligence: Step towards Computer Aided Diagnostic (CAD) in Healthcare Systems” International Conference on Software and Computer Applications (ICSCA) vol. 41 2012. Nalini Singh, Ambarish G Mohapatra, Gurukalyan Kanungo, “Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering”, International Journal Of Computer Applications(09758887),Volume 22 -No.2,May 2011. Narain Ponraj D, Evangelin Jenifer M, Poongodi P, Samuel Manoharan J, “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer”, Journal of Emerging Trends in Computing and Information Sciences , ISSN 2079-8407,Vol. 2, No. 12, December 2011. Pradeep Kumar, Rajat Chaudhary, Ambika Aggarwal, Prem Singh, Ravi Tomar , “Improving Medical Image Segmentation Techniques Using Multiphase Level Set Approach Via Bias Correction”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-5, June 2012. Radhika Siva Ramakrishna “Imaging Techniques Alternative to Mammography for Early Detection of Breast Cancer” Technology in Cancer Research & Treatment ISSN 1533-0346 Volume 4, Number 1, February (2005) ©Adenine Press, 2005. Rashedi E, Nezamabadi-Pour H, Saryazdi S, “GSA: A gravitational search algorithm”, Information Science, 179 (13), 2232-2248, 2009. Roman Timofeev, Wolfgang Hardle, “Classification and Regression Trees (CART) Theory and Its Applications”, A Master Thesis Presented by CASE - Center of Applied Statistics and Economics,Dec 2004. Samir Kumar Bandyopadhyay, “Pre-processing of Mammogram Images”, International Journal of Engineering Science and Technology Vol. 2(11), 2010. Senthil kumaran N, Rajesh R, “Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009. Shruti Dalmiya, Avijit Dasgupta, Soumya Kanti Datta, “Application of Wavelet based K-means Algorithm in Mammogram Segmentation”, International Journal of Computer Applications (0975 – 8887) Volume 52– No.15, August 2012. Siva Kumar R, Marcus Karnan, “Diagnose Breast Cancer through Mammograms Using EABCO Algorithm”, International Conference and workshop on Emerging Trends in Technology (ICWET), 2011. Soumi Ghosh, Sanjay Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No.4, 2013. Subash Chandra Bose J, Kumar KRS, Karnan M, “Detection of Microcalcification in Mammograms using Soft Computing Techniques”, European Journal of Scientific Research 86 (1), 103-122, 2012. Sundarami M, Ramar K, Arumugami N, Prabini G, “Histogram Based Contrast Enhancement For Mammogram Images”, International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN),2011. Thangavel K, Karnan M, “Automatic Detection of Asymmetries in Mammograms Using Genetic Algorithm”, International Journal on Artificial Intelligence and Machine Learning 5 (3), 55-62, 2005 Thangavel K, Karnan M, “CAD system for Preprocessing and Enhancement of Digital Mammograms”, International Journal on Graphics Vision and Image Processing 5 (9), 69-74, 2005.
ISSN: 2231-2803
[46]
[47]
[48]
[49]
[50]
Thangavel K, Karnan M, “Computer aided diagnosis in digital mammograms: detection of microcalcifications by meta heuristic algorithms”, GVIP Journal 5 (7), 41-55, 2005. Thangavel K, Karnan M, Pethalakshmi A, “Performance analysis of rough reduce algorithms in mammogram”, International Journal on Global Vision and Image Processing 5 (8), 13-21, 2005. Thangavel K, Karnan M, Siva Kumar R, KajaMohideen A, “Ant Colony System for Segmentation and Classification of Microcalcification in Mammograms”, International Journal on Artificial Intelligence and Machine Learning, Vol 5, Issue 3, Pages 29-40,2005. Thangavel K, Karnan M, Siva Kumar R, KajaMohideen A, “Automatic Detection of Microcalcification in Mammograms– A Review”, ICGST International Journal on Graphics, Vision and Image Processing, 2005. Thangavel K, Karnan M, Siva Kumar R, Mohideen AK, “Segmentation and classification of microcalcification in mammograms using the ant colony system”, International Journal on Artificial Intelligence and Machine Learning, 5 (3), 29-40, 2005.
http://www.ijcttjournal.org
Page147