Artificial Neural Network Based Spermatozoa Classification Using First Order Statistics and GLCM Features
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WHO,” Laboratory Manual for the Examination and processing of human semen”, Fifth Edition, WHO 2010.
Anna N. Karahaliou et. al., “Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications”, IEEE Transactions on Information Technology in Biomedicine, Volume: 12, Issue: 6, pp. 731 – 738, Nov. 2008.
M. Hidalgo et al., “Effect of sample size and staining methods on stallion sperm morphometry by the Sperm Class Analyzer”, Vet. Med. – Czech, 50, 2005 (1): 24–32.
Haralick, R. M., Shanmugam, K., and Dinstein, I., “Textural Features for image classification”, IEEE Transaction on System, man and Cybermatics, Vol. 3, No. 6. pp. 610 – 621, 1973.
http://www.vivo.colostate.edu/hbooks/pathphys/reprod/semeneval/morph.html
Carrillo, H. et al., “A Computer Aided Tool for the Assessment of Human Sperm Morphology”, International Conference on Bioinformatics and Bioengineering, 978-1-4244-1509-0, BIBE 2007.
Enrique Alegre et al., “Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ”, Computers in Biollogy and Medicine, 2008 April, 38(4):461-8. Epub 2008 Mar 14.
Lidia Sanchez et al., “Classification and quantification based on Image analysis for sperm samples with uncertain Damaged/ Intact Cell proportions”, International conference on Image analysis and recognition, ICIAR 2008.
Jia Lu, Yunxia Hu , ”Soft Computing for Human Spermatozoa Morphology”, Systemics, Cybernetics And Informatics, Vol. 3 No. 3.
D.E. Kime, et al., “Computer-assisted sperm analysis (CASA) as a tool for monitoring sperm quality in fish”, Comparative Biochemistry and Physiology Part C 130(2001) pp. 425-433.
D S Guru et. al.,”Texture Features and KNN in Classification of Flower Images”, IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition, RTIPPR, 2010.
Basavaraj .S. Anami, et. al.,“Texture based Identification and Classification of Bulk Sugary Food Objects”, ICGST-GVIP Journal, ISSN: 1687-398X, Volume 9, Issue 4, August 2009.
P. Babaghorbani, et. al., “Sonography Images for Breast Cancer Texture classification in Diagnosis of Malignant or Benign Tumors”, 978-1-4244-4713-8/10/$25.00 ©2010 IEEE.
A. Khoshroo et. al., ” Classification of Pomegranate Fruit using Texture Analysis of MR Images”, Agricultural Engineering International: the CIGR Ejournal. Manuscript 1182. Vol. XI., March 2009.
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