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Innovation in Biomedical Science and Engineering

Cervical cancer histology image identification method based on texture and lesion area features

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References

  • Guo P, Banerjee K, Joe Stanley R, et al. Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE J Biomed Health Inform. 2016;20:1595–1607.
  • Payel Rudra P, Mrinal Kanti B, Debotosh B. Automated cervical cancer using pap smear images. Adv Intell Syst Comput. 2015;335:267–278.
  • Yin-Hai W, Crookes D, Eldin OS, et al. Assisted diagnosis of cervical intraepithelial neoplasia (CIN). IEEE J Sel Top Signal Process. 2009;3:112–121.
  • De S, Joe Stanley R, Lu C, et al. A fusion-based approach for uterine cervical cancer histology image classification. Comput Med Imaging Graph. 2013;37:475–487.
  • Gisele Helena Barboni M, Junior B, Edson Garcia S, et al. Structural Analysis of Histological Images to Aid Diagnosis of Cervical Cancer. 25th SIBGRAPI Conference on Graphics, Patterns and Images. 2012 August 22-25; Ouro Preto, Brazil.
  • Keenan SJ, Diamond J, McCluggage WG, et al. An Automated Machine Vision System for The Histological Grading of Cervical Intraepithelial Neoplasia (CIN). J Pathol. 2000;192:351–362.
  • Miranda GHB, Soares EG, Barrera J, et al. Method to Support Diagnosis of Cervical Intraepithelial Neoplasia (CIN) Based on Structural Analysis of Histological Images. Proceedings of the IEEE Symposium on Computer-Based Medical Systems (CBMS); 2012. p. 1–6.
  • Yi-Ying W, S-C, Chang L-W, Wu, et al. A Color-Based Approach for Automated Segmentation in Tumor Tissue Classification. Proceedings of The 29th Annual International Conference of The IEEE EMBS. 2007 August 23-26; Lyon, France.
  • Guillaud M, Adler-Storthz K, Malpica A, et al. Subvisual chromatin changes in cervical epithelium measured by texture image analysis and correlated with HPV. Gynecol Oncol. 2006;99:16–23.
  • Veta M, van Diest PJ, Kornegoor R, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS One. 2013;8:e70221.
  • Jalal Fadili M, Starck J-L, Bobin J. Image decomposition and separation using sparse representations: an overview. Proc IEEE. 2010;98:983–994.
  • Li-Wei K, Chia-Wen L, Yu-Hsiang F. Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process. 2012;21:1742–1755.
  • Panetta K, Bao L, Agaian S. Sequence-to-sequence similarity-based filter for image denoising. IEEE Sensors J. 2016;16:4380–4388.
  • Yu-Hua L, Shi-Jinn H, Seltzer J. Parallel computation of the euclidean distance transform on a three-dimensional image array. IEEE Trans Parallel Distrib Syst. 2003;14:203–212.
  • Bonnassie F, Peyrin D. Attali A. A new method for analyzing local shape in three-dimensional images based on medial axis transformation. IEEE Trans Syst, Man, Cybern B. 2003;33:700–705.
  • Lee DT. Medial axis transformation of a planar shape. IEEE Trans Pattern Anal Mach Intell. 1982;4:363–369.
  • Wang S, Rosenfeld A, Wu AY. A medial axis transformation for grayscale pictures. IEEE Trans Pattern Anal Mach Intell. 1982;4:419–421.
  • Jian Y, Jing-Feng G. Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix. International Conference on Virtual Reality and Visualization (ICVRV). 2011 November 4-5; Beijing, China.
  • Deepti Yadav M. Partha Sarathi, Malay Kishore Dutta. Classification of Glaucoma Based on Texture Features Using Neural Networks. Seventh International Conference on Contemporary Computing (IC3). 2014 August 7-9; Noida, India.
  • Owen KK, Wong DW. An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility Metrics. Appl Geography. 2013;38:107–118.
  • Dragut L, Csillik O, Eisank C, et al. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J Photogramm Remote Sens. 2014;88:119–127.
  • Nurhayati OD, Susanto A, Widodo TS, et al. Principal component analysis combined with first order statistical method for breast thermal images classification. Int Eng Technol Res J. 2011;2:72–78.
  • Lewis SH, Dong A-J. Detection of Breast Tumor Candidates Using Marker-Controlled Watershed Segmentation and Morphological Analysis. IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). 2012 April 22-24; Santa Fe, NM, USA.
  • Sivagami M, Revathi T. Marker Controlled Watershed segmentation Using Bit-Plane Slicing. Int J Image Process Vision Sci. 2012;1:6–10.
  • Peng-Fei S, Wen-Jian Q, Jie Y, et al. Segmenting Multiple Overlapping Nuclei in H&E Stained Breast Cancer Histopathology Images Based on An Improved Watershed. IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015). 2015 November 19-19; Beijing, China.
  • Tzortzis G, Likas A.The Global Kernel K-means Clustering Algorithm. IEEE World Congress on Computational Intelligence. 2008 June 1-8; Hong Kong, China.
  • Rahmani M, Akbarizadeh G. Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images. IET Comput Vision. 2015;9:629–638.
  • Alush A, Goldberger J. Hierarchical image segmentation using correlation clustering. IEEE Trans Neural Netw Learning Syst. 2016;27:1358–1367.
  • Lilla B, Silvia M, Alessia B, et al. Cluster Analysis Boosted Watershed Segmentation of Neurological Image. 4th International Congress on Image and Signal Processing (CISP). 2011 October 15-17; Shanghai, China.
  • Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol. 2011;2:1–27.
  • Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998; 2:121–167.