A Semi-Supervised Method for Tumor Segmentation in Mammogram Images
DOI: 10.4103/jmss.JMSS_62_18
Abstract
Background: Breast cancer is one of the most common cancers in women. Mammogram images have an important role in the treatment of various states of this cancer. In recent years, machine learning methods have been widely used for tumor segmentation in mammogram images. Pixelbased segmentation methods have been presented using both supervised and unsupervised learning approaches. Supervised learning methods are usually fast and accurate, but they usually use a large number of labeled data. Besides, providing these samples is very hard and usually expensive. Unsupervised learning methods do not require the labels of the training data for decision making and they completely ignore the prior knowledge that may lead to a low performance. Semi-supervised learning methods which use a small number of labeled data solve the problem of providing the high number of samples in supervised methods, while they usually result in a higher accuracy in comparison to the unsupervised methods. Methods: In this study, we used a semisupervised method for tumor segmentation in which the pixel information is used for the classification. The static and gray level run length matrix features for each pixel are considered as the features, and Fisher discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support vector machine and Bayes classifiers is proposed for tumor segmentation on MIAS data set. Results and Conclusion: The results show that the proposed method outperforms both supervised methods.
Keywords
Full Text:
PDFReferences
Azmi R, Norozi N, Anbiaee R, Salehi L, Amirzadi A. IMPST: A New interactive self-training approach to segmentation suspicious lesions in breast MRI. J Med Signals Sens 2011;1:138-48.
Nahid AA, Kong Y. Involvement of machine learning for breast cancer image classification: A survey. Comput Math Methods Med 2017;2017:3781951.
Cordeiro FR, Saki F, Silva-Filho AG, Pinheiro Dos Santos W. Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images. Comput Methods Biomech Biomed Eng 2017;5:297-315.
Cordeiro FR, Santos WP, Silva-Filho AG. A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Syst Appl 2016; 65:116-26.
Oliver A, Freixenet J, Marti J, Perez E, Pont J, Denton ER, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010;14:87-110.
Behrens S, Laue H, Althaus M, Boehler T, Kuemmerlen B, Hahn HK. Computer assistance for MR based diagnosis of breast cancer: Present and future challenges. Comput Med Imaging Graph 2007;31:236-47.
Dabass J, Arora S, Vig R, Hanmandlu M. Segmentation Techniques for Breast Cancer Imaging Modalities-A Review. 9th International Conference on Cloud Computing, Data Science and Engineering; 2019.
Eltonsy NH, Tourassi GD, Elmaghraby AS. A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging 2007;26:880-9.
Arefan D, Talebpour A, Aghamiri M. Diagnostic of Mass and Abnormal Areas in Mammography Images Using Chebyshev Moments. 1st MEFOMP International Conference of Medical Physics; 2011.
Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski L, Helvie MA. Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys 2008;35:280-90.
Berber T, Alpkocak A, Balci P, Dicle O. Breast mass contour segmentation algorithm in digital mammograms. Comput Methods Programs Biomed 2013;110:150-9.
Meenalosinin S. Segmentation of cancer cells in mammogram using region growing method and Gabor features. Int J Eng Res App 2012;2:1055-62.
Yuvraj K. Automatic Mammographic Mass Segmentation Based on Region Growing Technique. 3rd International Conference on Electronics, Biomedical Engineering and its Applications; April, 2013.
Gorgel P, Sertbas A, Ucan ON. Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme. Comput Biol Med 2013;43:765-74.
Varughese LS, Anitha J. A study of region based segmentation methods for mammograms. International Journal of Research in Engineering and Technology 2013;2:421-5.
Kozegar E, Soryani M, Behnam H, Salamati M, Tan T. Mass segmentation in automated 3-D breast ultrasound using adaptive region growing and supervised edge-based deformable model. IEEE Trans Med Imaging 2018;37:918-28.
Mencattini A, Lojacono L. Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrum Meas 2008;57:1422-30.
Narayan Panda R, Ketan Panigrahi B, Ranjan Patro M. Feature extraction for classification of microcalcifications and mass lesions in mammograms. Int J Comput Sci Net Secur 2009;9:255.
Vincent L. Watersheds in digital spaces: An efficient algorithm based on immersion simulation. IEEE Trans Pattern Anal Mach Intell 1991;13:583-98.
Herredsvela J, Engan K, Gulsrud TO, Skretting K. Detection of Masses in Mammograms by Watershed Segmentation and Sparse Representations Using Learned Dictionaries. Proceeding of NORSIG; 2005.
Sharma J, Sharma S. Mammogram image segmentation using watershed. Int J Info Tech and Knowledge Management 2011;4:423-5.
Bandyopadhyay S, Maitra I. Digital imaging in mammography towards detection and analysis of human breast cancer, IJCA Special Issue on Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications; 2010. p. 29-34.
Kanta Maitra I, Nag S, Kumar Bandyopadhyay S. Identification of abnormal masses in digital mammography images. Int J Comput Graph 2011;2:17-30.
Mohd Khuzi A, Besar R, Wan Zaki W, Ahmad N. Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed Imaging Interv J 2009;5:e17.
Tao Y, Lo SC, Freedman MT, Makariou E, Xuan J. Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms. Med Phys 2010;37:5993-6002.
Song E, Jiang L, Jin R, Zhang L, Yuan Y, Li Q, et al. Breast
mass segmentation in mammography using plane fitting and
dynamic programming. Acad Radiol 2009;16:826-35.
Song E, Xu S, Xu X, Zeng J, Lan Y, Zhang S, et al. Hybrid segmentation of mass in mammograms using template matching and dynamic programming. Acad Radiol 2010;17:1414-24.
Shi P, Zhong J, Rampun A, Wang H. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput Biol Med 2018;96:178-88.
Kamil MY, Salih AM. Mammography Images Segmentation via Fuzzy C-mean and K-mean. International Journal of Intelligent Engineering and Systems 2019;12:22-9.
Lucht R, Delorme S, Brix G. Neural network-based segmentation of dynamic MR mammographic images. Magn Reson Imaging 2002;20:147-54.
Azmi R, Pishgoo B, Norozi N, Yeganeh S. Ensemble semi-supervised frame-work for brain magnetic resonance imaging tissue segmentation. J Med Signals Sens 2013;3:94-106.
Saheb Basha S, Satya Prasad K. Automatic detection of breast
cancer mass in mammograms using morphological operator and fuzzy c-means clustering. J Theor Appl Inf Technol 2009;5:704-9.
Li HD, Kallergi M, Clarke LP, Jain VK, Clark RA. Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 1995;14:565-76.
Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003;50:92-8.
Tahmasbi A, Saki F, Shokouhi SB. Classification of benign and malignant masses based on zernike moments. Comput Biol Med 2011;41:726-35.
Amirzadi A, Azmi R. Introducing kernel based morphology as an enhancement method for mass classification on mammography. J Med Signals Sens 2013;3:117-26.
Ilin A, Raiko T. Practical approaches to principal component Azary and Abdoos: Semi-supervised method for tumor segmentation in mammogram images.
Tharwat A, Gaber T, Ibrahim A, Hassanien AE. Linear discriminant analysis: A detailed tutorial. AI communications. 2017;30:169-90.
Blum A, Tom M. Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory. New York, N.Y.: ACM; 1998. p. 92-100.
Dubey RB, Hanmandlu M, Gupta SK. A comparison of two methods for the segmentation of masses in the digital mammograms. Comput Med Imaging Graph 2010;34:185-91.
Saki F, Tahmasbi A, Soltanian-Zadeh H, Shokouhi SB. Fast opposite weight learning rules with application in breast cancer diagnosis. Comput Biol Med 2013;43:32-41.
Tahmasbi A, Saki F, Shokouhi SB. An Effective Breast Mass Diagnosis System Using Zernike Moment. Iran: Proceeding of 17th Iranian Conference Biomedical Engineering; 2010. p. 1-4.
Tahmasbi A, Saki F, Aghapanah H, Shokouhi SB. A Novel Breast Mass Diagnosis System Based on Zernike Moments as Shape and Density Descriptor. Tehran Iran: Proceeding of IEEE 18th Iran Conference Biomedical Engineering; 2011. p. 100-4.
Refbacks
- There are currently no refbacks.
https://e-rasaneh.ir/Certificate/22728
ISSN : 2228-7477