Effective Texture Features for Segmented Mammogram Images

  • Authors

    • P Anjaiah
    • K Rajendra Prasad
    • C Raghavendra
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16450
  • ROI, Mammogram segmentation, multi ROI segmentation.

  • Abstract

    Textures of mammogram images are useful for finding masses or cancer cases in mammography, which has been used by radiologist. Textures are greatly succeed for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) in most commonly used technique for mammogram segmentation. Limitation of this method is that it unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly for finding best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.

     

     

  • References

    1. [1] Shen-Chuan Tai, Zih-Siou Chen, and Wei-Ting Tsai, “An Automatic Mass Detection System in Mammograms Based on Complex Texture Features†IEEE Journal Of Biomedical And Health Informatics, VOL. 18, NO. 2, MARCH 2014

      [2] K. Rajendra Prasad, C. Raghavendra, K Sai Saranya, “A Review on Classification of Breast Cancer Detection using Combination of The Feature Extraction Modelsâ€, International Journal of Pure and Applied Mathematics, Volume 116 No. 21 2017, 203-208.

      [3] C. Raghavendra, Dr. A Kumaravel, A. Anjaiah, “A New Hybrid Method for Image De-Noising In Light Of Wavelet Transformâ€, International Journal of Pure and Applied Mathematics, Volume 116 No. 21 2017, 197-202.

      [4] Ye Lv; Guofeng Wang; Xiangyun Hu, “Automatic extraction of road regions of interest(ROI) from very high resolution remote sensing imagesâ€, 2016 3rd International Conference on Systems and Informatics (ICSAI), Pages: 976 – 981, 2016

      [5] K. Rajendra Prasad, C. Raghavendra, Effective Mammogram Classification Using Various Texture Features, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9. Sp– 12 / 2017.

      [6] K. Rajendra Prasad, C. Raghavendra, Padakandla Vyshnav, “Intelligent System for Visualized Data Analytics A Reviewâ€, International Journal of Pure and Applied Mathematics, Volume 116 No. 21 2017, 217-224.

      [7] X. Gao, Y. Wang, X. Li, and D. Tao, “On combining morphological component analysis and concentric morphology model for mammographic mass detection,†IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 266– 273, Mar. 2010.

      [8] C. Raghavendra, A. Kumaravel and S. Sivasuramanyan, “Features Subset Selection using Improved Teaching Learning based Optimisation (ITLBO) Algorithms for Iris Recognitionâ€, Indian Journal of Science and Technology, Vol 10(34), DOI: 10.17485/ijst/2017/v10i34/ 118307, September 2017.

      [9] D. H. Kim, J. Y. Choi, and Y. M. Ro, “Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection,†in Proc. 19th IEEE Int. Conf. Image Process., 2012, pp. 2821– 2824.

      [10] P. Kiran Kumar, C. Raghavendra, Dr. S. Sivasubramanyan, “Exploring Multi Scale Mathematical Morphology for Dark Image Enhancementâ€, International Journal of Pharmacy and Technology, Dec-2016, Vol. 8, Issue No.4, 23590-23597.

      [11] Xiang Zhong, Jingshan Li, , Susan M. Ertl, Carol Hassemer, and Lauren Fiedler, “A System-Theoretic Approach to Modeling and Analysis of Mammography Testing Processâ€, IEEE Transactions On Systems, Man, And Cybernetics: Systems, vol. 46, no. 1, 2016

      [12] C. Nalini, C. Raghavendra, K. Rajendra Prasad, “Comparative Observation and Performance Analysis of Multiple Algorithms on Iris Dataâ€, International Journal of Pure and Applied Mathematics, Volume 116 No. 9 2017, 319-325.

      [13] M. Bitenc, D. S. Kieffer , K. Khoshelham, “ Evaluation Of Wavelet Denoising Methods For Small-Scale Joint Roughness Estimation Using Terrestrial Laser Scanningâ€, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W5, 2015

      [14] J. Dengler, S. Behrens, and J. F. Desaga, “Segmentation of microcalcifications in mammograms,†IEEE Transactions on Medical Imaging, vol. 12, no. 4, pp. 634–642, 1993.

      [15] H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai, and H. N. Du, “Approaches for automated detection and classification of masses in mammograms,†Pattern Recognit., vol. 39, pp. 646–668, 2006.

      [16] C. J. Rose and C. J. Taylor, “A statistical model of texture for medical image synthesis and analysis,†Med. Image Understand. Anal., pp. 1–4, 2003.

      [17] N. Petrick, H.-P. Chan, D. Wei, B. Sahiner, M. A. Helvie, and D. D. Adler, “Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification,†Med. Phys., vol. 23, no. 10, pp. 1685–1696, 1996.

      [18] A. GarciaManso, C. GarciaOrellana, H. GonzalezVelasco, R. Gallardo- Caballero, and M. M. Macias, “Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction,†BioMed. Eng. Online, vol. 12, no. 1, 2013.

      [19] R. Nithya and B. Santhi, “Article: Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer,†Int. J.Comput. Appl., vol. 28, pp. 21–25, Aug. 2011.

      [20] C. Raghavendra, A. Kumaravel and S. Sivasuramanyan, “Identifying images on moving objects to enhance the recognitionâ€, Indian Journal of Science and Technology, Vol 7(1.5), pp. 279-282, December 2017.

      [21] K. Bovis; S. Singh, “Detection of masses in mammograms using texture featuresâ€, IEEE-15th International Conference on Pattern Recognition, 2000.

  • Downloads

  • How to Cite

    Anjaiah, P., Rajendra Prasad, K., & Raghavendra, C. (2018). Effective Texture Features for Segmented Mammogram Images. International Journal of Engineering & Technology, 7(3.12), 666-669. https://doi.org/10.14419/ijet.v7i3.12.16450

    Received date: 2018-07-28

    Accepted date: 2018-07-28

    Published date: 2018-07-20