|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Breast cancer computer aided diagnosis (CAD) systems can provide such help and they are important and necessary for breast cancer control. Micro calcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This research presents algorithms for building a classification system or CAD, especially to obtain the different characteristics of mass and micro calcification using association technique based on classification. Starting with an individual-specific deformable of 3D breast model, this modelling framework will be useful for tracking visible tumors between mammogram images, as well as for registering breast images taken from different imaging modalities. From the results, the classifier developed able to perform well by successfully classifying the cancer and non-cancer (normal) images with the accuracy of 97%. Apart from that, by applying color map to the final results of segmentation provides a more interesting display of information and gives more direction to the purpose of image processing, which distinguishes between cancerous and non-cancerous tissues. |
| References |
[1] Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: a cancer journal for clinicians, 61(2), 69-90. [2] Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., & Niklason, L. T. (2013). Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology, 267(1), 47-56. [3] Health Quality Ontario. (2016). Ultrasound as an adjunct to mammography for breast cancer screening: a health technology assessment. Ontario health technology assessment series, 16(15), 1. [4] Harms, S. E. (2001). Integration of breast MRI in clinical trials. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 13(6), 830-836. [5] Cox, R. F., Hernandez-Santana, A., Ramdass, S., McMahon, G., Harmey, J. H., & Morgan, M. P. (2012). Microcalcifications in breast cancer: novel insights into the molecular mechanism and functional consequence of mammary mineralisation. British journal of cancer, 106(3), 525. [6] Krupinski, E. A. (2010). Current perspectives in medical image perception. Attention, Perception, & Psychophysics, 72(5), 1205-1217. [7] Castellino, R. A. (2005). Computer aided detection (CAD): an overview. Cancer Imaging, 5(1), 17. [8] Cole, E. B., Zhang, Z., Marques, H. S., Edward Hendrick, R., Yaffe, M. J., & Pisano, E. D. (2014). Impact of computer-aided detection systems on radiologist accuracy with digital mammography. American Journal of Roentgenology, 203(4), 909-916. [9] Jalalian, A., Mashohor, S., Mahmud, R., Karasfi, B., Saripan, M. I. B., & Ramli, A. R. B. (2017). Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI journal, 16, 113. [10] The, J. S., Schilling, K. J., Hoffmeister, J. W., Friedmann, E., McGinnis, R., & Holcomb, R. G. (2009). Detection of breast cancer with full-field digital mammography and computer-aided detection. American journal of roentgenology, 192(2), 337-340. [11] Mordang, J. J., Gubern-Mérida, A., Bria, A., Tortorella, F., Mann, R. M., Broeders, M. J. M., & Karssemeijer, N. (2018). The importance of early detection of calcifications associated with breast cancer in screening. Breast cancer research and treatment, 167(2), 451-458. [12] Razek, N. M. A., Yousef, W. A., & Mustafa, W. A. (2013). Microcalcification detection with and without CAD system (LIBCAD): A comparative study. The Egyptian Journal of Radiology and Nuclear Medicine, 44(2), 397-404. [13] Stoeblen, F., Landt, S., Ishaq, R., Stelkens-Gebhardt, R., Rezai, M., Skaane, P., & Kuemmel, S. (2011). Highfrequency breast ultrasound for the detection of microcalcifications and associated masses in BI-RADS 4a patients. Anticancer research, 31(8), 2575-2581. [14] Biswas, R., Nath, A., & Roy, S. (2016, September). Mammogram Classification Using Gray-Level Cooccurrence Matrix for Diagnosis of Breast Cancer. In Micro-Electronics and Telecommunication Engineering (ICMETE), 2016 International Conference on (pp. 161-166). IEEE. [15] Vujasinovic, T., Pribic, J., Kanjer, K., Milosevic, N. T., Tomasevic, Z., Milovanovic, Z., & Radulovic, M. (2015). Gray-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk. Microscopy and Microanalysis, 21(3), 646-654. [16] Pratiwi, M., Harefa, J., & Nanda, S. (2015). Mammograms classification using gray-level cooccurrence matrix and radial basis function neural network. Procedia Computer Science, 59, 83-91. [17] Abdelrahman, A., & Hamid, O. (2012). Breast Ultrasound Images Enhancement Using Gray Level Co-Occurrence Matrices Quantizing Technique. International Journal of Information Science, 2(5), 60-64. [18] Lee, J., Reece, G. P., & Markey, M. K. (2012). Breast curvature of the upper and lower breast mound: 3D analysis of patients who underwent breast reconstruction. In 3rd International Conference on 3D Body Scanning Technologies(pp. 171-179). [19] Li, C. M., Segars, W. P., Tourassi, G. D., Boone, J. M., & Dobbins, J. T. (2009). Methodology for generating a 3D computerized breast phantom from empirical data. Medical physics, 36(7), 3122-3131. [20] Nalawade, Y. V. (2009). Evaluation of breast calcifications. Indian Journal of Radiology & Imaging, 19(4). [21] Rominger, M. B., Steinmetz, C., Westerman, R., Ramaswamy, A., & Albert, U. S. (2015). Microcalcification-Associated Breast Cancer: Presentation, Successful First Excision, Long-Term Recurrence and Survival Rate. Breast care, 10(6), 380-385. [22] Naseem, M., Murray, J., Hilton, J. F., Karamchandani, J., Muradali, D., Faragalla, H., ... & Brezden-Masley, C. (2015). Mammographic microcalcifications and breast cancer tumorigenesis: a radiologic-pathologic analysis. BMC cancer, 15(1), 307. [23] Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., ... & Taylor, P. (2015). Mammographic Image Analysis Society (MIAS) database v1. 21. [24] Herwanto, A. M. A., & Arymurthy, A. M. (2013). Association technique based on classification for classifying microcalcification and mass in mammogram. IJCSI International Journal of Computer Science Issues, 10(1), 1694-0814. [25] Gur, D., Abrams, G. S., Chough, D. M., Ganott, M. A., Hakim, C. M., Perrin, R. L., ... & Bandos, A. I. (2009). Digital breast tomosynthesis: observer performance study. American Journal of Roentgenology, 193(2), 586-591. [26] Bruno, D. O. T., do Nascimento, M. Z., Ramos, R. P., Batista, V. R., Neves, L. A., & Martins, A. S. (2016). LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Systems with Applications, 55, 329-340. [27] Bandyopadhyay, S. K. (2010). Pre-processing of Mammogram Images. International Journal of Engineering Science and Technology, 2(11), 6753-6758. [28] Bandyopadhyay, S. K. (2010). Detection of abnormal masses in mammogram images. International Journal of Computer Science and Information Technologies, 1(5), 438-442. [29] Bandyopadhyay, S. K., Maitra, I. K., & Kim, T. H. (2011, April). Identification of abnormal masses in digital mammography images. In Ubiquitous Computing and Multimedia Applications (UCMA), 2011 International Conference on (pp. 35-41). IEEE. [30] Ponraj, D. N., Jenifer, M. E., Poongodi, P., & Manoharan, J. S. (2011). A survey on the preprocessing techniques of mammogram for the detection of breast cancer. Journal of Emerging Trends in Computing and Information Sciences, 2(12), 656-664. [31] M. Sezgin & B. Sankur (2004). "Survey over image thresholding techniques and quantitative performance evaluation". Journal of Electronic Imaging. 13 (1): 146– 165. doi:10.1117/1.1631315. [32] Liu, Dongju (2009). "Otsu method and K-means". Ninth International Conference on Hybrid Intelligent Systems IEEE. 1: 344–349. [33] Al-Bayati, M., & El-Zaart, A. (2013). Mammogram images thresholding for breast cancer detection using different thresholding methods. Advances in Breast Cancer Research, 2(03), 72. [34] Shanmugavadivu, P., & Narayanan, S. L. (2013). Segmentation of microcalcifications in mammogram images using intensity-directed region growing. In Computer Communication and Informatics (ICCCI), 2013 International Conference on (pp. 1-6). IEEE. [35] Swetha, T. L. V. N., & Bindu, C. H. (2015). Detection of Breast cancer with Hybrid image segmentation and Otsu's thresholding. In Computing and Network Communications (CoCoNet), 2015 International Conference on (pp. 565-570). [36] Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International journal of scientific and research publications, 3(5), 1. [37] Elmore, J. G., Armstrong, K., Lehman, C. D., & Fletcher, S. W. (2005). Screening for breast cancer. Jama, 293(10), 1245-1256
|
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |