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Articles

Breast Tumor Detection in Digital Mammogram Based on Efficient Seed Region Growing Segmentation

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References

  • A. Jemal, R. Siegel, and E. Ward, “Cancer statistics, 2008,” CA Cancer J. Clin., Vol. 58, no. 2, pp. 71–96, 2008.
  • L. Hutchinson, “Breast cancer challenges, controversies, breakthroughs,” Nat. Rev. Clin. Oncol., Vol. 7, pp. 669–70, December 2010.
  • Z. Wang, G. Yu, Y. Kang, Y. Zhao, and Q. Qu, “Breast tumor detection in digital mammography based on extreme learning machine,” Neurocomputing, Vol. 128, no. 5, pp. 175–84, 2014.
  • G. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. Syst. Man Cyber, Part B: Cybern, Vol. 42, no. 2, pp. 513–29, 2012.
  • J. Chu, H. Min, L. Liu, and W. Lu, “A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC super pixel segmentation,” Med. Phys., Vol. 42, no. 7, pp. 3859–69, 2015.
  • A. Oliver, J. Freixenet, J. Marti, E. Perez, J. Pont, E. R. Denton, and R. Zwiggelaar, “A review of automatic mass segmentation of masses detection and segmentation in mammographic images,” Med. Image Anal., Vol. 14, no. 2, pp. 87–100, 2010.
  • J. Tang, R. M. Rangayyan, J. Xu, L. E. I. Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” Inf. Technol. Biomed. IEEE Trans., Vol. 13, no. 2, pp. 236–51, 2009.
  • C. Gallego-Ortiz, and A. L. Martel, “Improving the accuracy of computer-aided diagnosis for breast MR imaging by differentiating between mass and non-mass lesions,” Radiology, Vol. 278, no. 3, pp. 679–88, 2015.
  • H. Wang, J. Feng, Q. Bu, F. Liu, M. Zhang, Y. Ren, and Y. Lv, “Breast mass detection in digital mammogram based on Gestalt psychology,” Hindwai J. Healthc. Eng., Vol. 2018, pp. 1–13, 2018.
  • L. Zheng, and A. K. Chan, “An artificial intelligent algorithm for tumor detection in screening mammogram,” IEEE Trans. Med. Imaging, Vol. 20, no. 7, pp. 559–67, July 2001.
  • N. N. Shah, T. V. Ratanpara, and C. K. Bhensdadia, “Early breast cancer tumor detection on mammogram images,” Int. J. Comput. Appl., Vol. 87, no. 14, pp. 114–20, Feburary 2014.
  • S. M. Badawy, A. A. Hefnawy, H. E. Zidan, and M. T. Gadallah, “Breast cancer detection with mammogram segmentation: A qualitative study,” Int. J. Adv. Comput. Sci. Appl., Vol. 8, no. 10, pp. 117–20, 2017.
  • N. Dhungel, G. Carneiro, and A. P. Bradley, “A deep learning approach for the analysis of masses in mammograms with minimal user intervention,” Med. Image Anal., Vol. 37, pp. 114–28, 2017.
  • P. Casti, A. Mencattini, M. Salmeri, A. Ancona, F. Mangeri, M. L. Pepe, and R. M. Rangayyan, “Contour independent detection and classification of mammographic lesions,” Biomed. Signal. Process. Control., Vol. 25, pp. 165–77, 2016.
  • N. Petrick, H. P. Chan, B. Sahiner, and D. Wei, “An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection,” IEEE Trans. Med. Imaging, Vol. 15, no. 1, pp. 59–67, 1996.
  • R. Adams, and L. Bischof, “Seed region growing,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 16, pp. 641–7, 1994.
  • A. Melouah, and S. Layachi. “A novel automatic seed placement approach for region growing segmentation in mammograms” IPAC’15 November 23–25, 2015, Batna, Algeria @2015 ACM. ISBN: 978-1-4503-3458-7/15/11.
  • M. M. Saleek, and A. EI Moutaouakkil, “Automatic Seeded region growing based on texture features for mass segmentation in digital mammography,” Int. Conf. Inf. Technol. Commun. Syst., Adv. Intell. Syst. Comput., Vol. 640, pp. 331–8, 2017.
  • J. Suckling, et al. Mammographic Image Analysis Society (MIAS) database v1.21 [Dataset] 2015. https://www.repository.cam.ac.uk/handle/1810/250394.
  • F. Y. Shih, and S. Cheng, “Automatic seeded region growing for color image segmentation,” Image. Vis. Comput., Vol. 23, pp. 877–86, 2005.
  • A. Qusay, et al. “Breast MRI tumor segmentation using modified automatic seeded region growing based on particle swarm optimization image clustering” Soft Computing in Industrial Applications, Advances in Intelligent systems and computing 223, DOI: 10.1007/978-3-319-00930-8-5 @Springer International Publishing Swizerland 2014.
  • J. Wang, and Y. Yang, “A context sensitive deep learning approach for micro calcification detection in mammograms,” Pattern Recognit., Vol. 78, pp. 12–22, 2018.
  • Z. Wang, Q. Qu, G. Yu, and Y. Kang, “Breast tumor detection in double views mammography based on extreme learning machine,” Neural Comput. & Applic., Vol. 27, pp. 227–240, 2016, Springer. doi: 10.1007/s00521-014-1764-0
  • N. Shrivastava, and J. Bharti. “Empirical analysis of image segmentation techniques”, © Springer Nature Singapore Pte Ltd. 2016, SmartCom 2016, CCIS 628, pp. 143–50, 2016 DOI: 10.1007/978-981-10-3433-6_18.
  • N. Shrivastava, and J. Bharti, ““A comparative analysis of medical image segmentation”, International Conference on Advanced Computing Networking and Informatics,” Adv. Intell. Syst. Comput., Vol. 870, pp. 459–67, 2019. doi:10.1007/978-981-13-2673-8_48.
  • V. P. Singh, A. Srivastava, D. Kulshreshtha, A. Choudhary, and R. Shrivastava, “Mammogram classification using selected GLCM feature and random forest classifier,” Int. J. Comput. Sci. Inf. Secur. (IJCSIS), Vol. 14, no. 6, pp. 82–87, June 2016.
  • V. P. Singh, and R. Srivastava, “Content based mammogram retrieval using wavelet based complete – LBP and K-means clustering for the diagnosis of breast cancer,” Int. J. Hybrid. Intell. Syst., Vol. 14, pp. 31–39, 2017.
  • V. P. Singh, S. Srivastava, and R. Shrivastava, “Automated and effective content-based image retrieval for digital mammography,” J. Xray. Sci. Technol., Vol. 26, pp. 29–49, 2018. doi:10.3233/XST-17306. IOS Press.
  • V. P. Singh, and R. Srivastava, “Automated and effective content based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map,” Boicybern. Biomed. Eng., 2017. doi:10.1016/j.bbe.2017.09.03.
  • V. P. Singh, S. Srivastava, and R. Srivastava, “Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests,” Technol. Health Care, Vol. 25, pp. 709–27, 2017. doi:10.3233/THC-170851. IOS Press.
  • A. Makandar, and B. Halalli, “Pre-processing of mammography image for early detection of breast cancer,” Int. J. Comput. Appl. (0975-8887), Vol. 144,, no. 3, pp. 11–15, June 2016.
  • K. L. Kashyap, M. K. Bajpai, and P. Khanna, “An Efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms,” Multimed. Tools Appl., Vol. 77, pp. 9249–69, Spring, 2017.
  • A. Ramya, D. Murugan, and G. Muthulakshmi, “Implementation and comparison of the spatial denoising filter for impulse noise on MIAS dataset,” Int. J. Comput. Sci. Eng., Vol. 6, no. 9, pp. 510–4, Sep 2018.
  • M. P. Sukassini, and T. Velmurugan. “Noise removal using morphology and median filter methods in mammogram images,” The 3rd International Conference on Small & Medium Business 2016 January 19–21, 2016, Nikko Saigon Hotel, Hochiminh, Vietnam, pp. 413–519.
  • K. L. Kashyap, K. K. Singh, M. K. Bajpai, and P. Khanna. “Fractional order filter based enhancement of digital mammograms,” Proceedings of the World Congress on Engineering and Computer Science 2017 Vol I WCECS 2017, October 25–27, 2017, San Francisco, USA.
  • S. Kannan, N. P. Subiramaniyam, A. T. Rajamanickam, and A. Balamurugan, “Performance comparison of noise reduction in mammogram images,” Int. J. Res. Eng. Technol., Vol. 05, no. 2, pp. 31–33, Feb 2016.
  • D. Surya Prabha, and J. Satheesh Kumar, “Performance evaluation of image segmentation using objective methods,” Indian J. Sci. Technol., Vol. 9, no. 8, pp. 1–8, February 2016. ISSN: 0974-5645.
  • Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recognit., Vol. 29, no. 8, pp. 1335–46, 1996. Elsevier Science Ltd.
  • M. Polak, H. Zhang, and M. Pi, “An evaluation metric for image segmentation of multiple objects,” Image. Vis. Comput., Vol. 27, pp. 1223–7, 2009.
  • P. Kalavathi, “An image spatial alignment method for computing region overlapping measures,” Int. J. Emerg. Trends Technol. Comput. Sci., Vol. 3, no. 5, pp. 251–5, September 2014. ISSN: 2278-6856.
  • S. M. Aqil Burney, and H. Tariq, “K-Means Cluster analysis for image segmentation,” Int. J. Comput. Appl., Vol. 96, pp. 1–8, June 2014. ISSN: 0975-8887.
  • A. Phophalia, S. K. Mitra, and C. Chawla, “A study on image segmentation using moments,” Asian J. Comput. Sci. Inf. Technol., Vol. 2, pp. 89–93, 2012. ISSN: 2249-5126.
  • M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer. “The digital database for screening mammography,” in Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–8, Toronto Canada June 2000.
  • P. S. Vikhe, and V. R. Thool, “Mass detection in mammographic images using wavelet processing and adaptive threshold technique,” J. Med. Syst., Vol. 40, no. 4, p. 82, 2016.
  • N. M. Basheer, and M. M. H. Mohammed, “Segmentation of breast masses in digital mammograms using adaptive median filtering and texture analysis,” Int. J. Recent Technol. Eng., Vol. 2, no. 1, pp. 39–43, 2013.
  • P. U. Hepsag, S. A. Ozel, and A. Yazici. “Using deep learning for mammography classification,” 2nd International Conference on Computer Science and Engineering, October 5–8, 2017, Antalya, Turkey, IEEE.
  • S. Dhahbi, W. Barhoumi, and E. Zagruoba, “Breast cancer diagnosis in digitized mammograms using curvelet moments,” Comput. Biol. Med., Vol. 64, pp. 79–90, 2015 Sep 1.
  • P. Gorge, A. Sertbas, N. Kilic, N. Osman, and O. Osman, “Mammographical mass detection and classification using wavelet based support vector machine,” Methods, Vol. 10, pp. 11, 2012 Feb 14.
  • L. Liu, J. Li, and Y. Wang. “Breast mass detection with kernelized supervised hashing”, International Conference on Biomedical Engineering and Informatics, pp. 79–84, Shenyang, China, 2016.
  • M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. Philip Kegelmeyer. “The Digital Database for Screening Mammography”, In Proceedings of the Fifth International Workshop on Digital Mammography, Toronto. Medical Physics Publishings, June 11–14, 2000, pp. 212–218.

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