CFP last date
22 April 2024
Reseach Article

A Collaborative Biomedical Image-Mining Framework along with Image Annotation

by Kamalpreet Kaur, Ada
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 13
Year of Publication: 2015
Authors: Kamalpreet Kaur, Ada
10.5120/20398-2699

Kamalpreet Kaur, Ada . A Collaborative Biomedical Image-Mining Framework along with Image Annotation. International Journal of Computer Applications. 116, 13 ( April 2015), 25-28. DOI=10.5120/20398-2699

@article{ 10.5120/20398-2699,
author = { Kamalpreet Kaur, Ada },
title = { A Collaborative Biomedical Image-Mining Framework along with Image Annotation },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 13 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number13/20398-2699/ },
doi = { 10.5120/20398-2699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:02.903187+05:30
%A Kamalpreet Kaur
%A Ada
%T A Collaborative Biomedical Image-Mining Framework along with Image Annotation
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 13
%P 25-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skin is the largest organ in our body. Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. If the abnormal cell is not controlled, it can result in death. There are two types of skin cancer: malignant melanoma of the skin, and non-melanoma skin cancer (NMSC). Malignant melanoma is the less common but most serious type of skin cancer. In this paper survey how to detect skin cancer in efficient manner and his detail what kind of skin cancer it is. ??

References
  1. N. Howlader, A. M. Noone, M. Krapcho, J. Garshell, N. Neyman, S. Altekruse, C. L. Kosary, M. Yu, J. Ruhl, Z. Tatalovich, H. Cho, A. Mariotto, D. R. Lewis, H. S. Chen, E. J. Feuer, and K. A. Cronin, "SEER cancer statistics review, 1975-2010," Nat. Cancer Inst. , Bethesda, MD,USA, Tech. Rep. , 2013
  2. R. H. Johr, Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy ,menzies scoring method, and7-pointchecklist, Clinics in Dermatology 20(3)(2002)240–247.
  3. H. Blum, Luedtke, U. Ellwanger, etal. ,Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837melanocyticlesions, BritishJournalofDermatology 151 (5)(2004)1029–1038.
  4. G. Argenziano,H. P. Soyer,S. Chimenti,R. Talamini,R. Corona,F. Sera,etal. ,Dermoscopy of pigmented skin lesions :results of a consensusmeeting via the Internet,Journal of the American Academy ofDermatology48(5)(2003)679–693.
  5. M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, and R. H. Moss, "A methodological approach to the classification of dermoscopy images," Comput. Med. Imag. Graph. , vol. 31, no. 6, pp. 362–373, 2007.
  6. H. Ganster, A. Pinz, R. R¨uhrer, E. Wildling, M. Binder, and H. Kittler,"Automated melanoma recognition," IEEE Trans. Med. Imag. , vol. 20, no. 3, pp. 233–239, Mar. 2001.
  7. S. Shan, W. Gao, B. Cao, and D. Zhao, "Illumination normalization for robust face recognition against varying lighting conditions," in Proc. IEEE Int. Workshop Anal. Model. Faces Gesture, Oct. 2003, pp. 157–164.
  8. J. Glaister, A. Wong, and D. A. Clausi, "Illumination correction in dermatological photographs using multistage illumination modeling for skin lesion analysis," in Proc. EEE 34th Annu. Int. Conf. IEng. Med. Biol. Soc. , Aug. /Sep. 2012, pp. 102–105.
  9. A. Smith, "Color gamut transform pairs," ACM SIGGRAPH Comput. Graph. , vol. 12, no. 3, pp. 12–19, 1978.
  10. Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision. Cengage-Engineering 2007.
  11. M. Anantha, R. H. Moss, and W. V. Stoecker, "Detection of pigment network in dermatoscopy images using texture analysis," Comput. Med. Imag. Graph. , vol. 28, no. 5, pp. 225–234, 2004.
  12. G. Peyre, "Sparsemodeling of textures," J. Math. Imag. Vis. , vol. 34, no. 1, pp. 17–31, 2009.
  13. T. Glatard, J. Montagnat, and X. Pennec, "Grid-enabled workflows for data intensive medical applications," in Proc. 18th IEEE Symp. Computer- Based Med. Syst. , Jun. 2005, pp. 537–542.
  14. D. Kreftin, M. Vossberg, A. Hoheisel, and T. Tolxdorff, "Simplified implementation of medical image processing algorithms into a grid using a workflow management system," J. Future Gener. Comput. Syst. , vol. 26, no. 4, pp. 681–684, Apr. 2010.
  15. C. Botha, "DeVIDE—the Delft visualization and image processing development environment," Technical University of Delft, Delft, The Netherlands, Tech. Rep. http://graphics. tudelft. nl, May 2005.
  16. S. Koulouzis, E. Zudilova-Seinstra, and A. Belloum, "Data transport between visualization web services for medical image analysis," Procedia Comput. Sci. , vol. 1, no. 1, pp. 1727–1736, May 2010.
  17. José Fernández Alcón, C?alina Ciuhu, Warner ten Kate, Adrienne Heinrich, Natallia Uzunbajakava, Gertruud Krekels, Denny Siem, and Gerard de Haan" Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis" IEEE journal of selected topics in signal processing, vol. 3, no. 1, february 2009
  18. Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, et al. The abcd rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions. J Am Acad Dermatol 1994;30:551–9.
  19. Mariam, A. Sheha,Mai, S. Mabrouk, Amr Sharawy, "Automatic Detection of Melanoma Skin Cancer using Texture Analysis", International Journal of Computer Applications, Volume 42, 2012.
  20. S. Li, J. T. Kwok, H. Zhu, Y. Wang, Texture classification using the support vector machines, Pattern Recognition 36 (12) (2003) 2883–2893.
  21. A. Sengur, Wavelets transform and adaptive neuro-fuzzy inference system for color texture classification, Expert Systems with Applications 34 (3) (2008) 2120–2128.
  22. G. M. Haley, B. S. Manjunath, Rotation-invariant texture classification using a complete space-frequency model, IEEE Transactions on Image Processing 8 (2) (1999) 225–269.
  23. H. Zhou, R. Wang, C. Wang, A novel extended local binary pattern operator for texture analysis, Information Sciences 178 (22) (2008) 4314–4325.
  24. W. T. Freeman, E. H. Adelsonk, The design and use of steerable filters, IEEE Transactions on Pattern Analysisand Machine Intelligence 13 (9) (1991) 891–906.
  25. 25. E. P. Simoncelli, W. T. Freeman, E. H. Adeslon, D. J. Heeger, Shiftable multi-scale transforms, IEEE Transactions on Information Theory 38 (2) (1992) 587–607.
  26. Jeffrey Glaister?, Student Member, IEEE, Robert Amelard, Student Member, IEEE, Alexander Wong, Member, IEEE, and David A. Clausi, Senior Member, IEEE" MSIM: Multistage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis" IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 7, JULY 2013 1873
  27. José Fernández Alcón, C?alina Ciuhu, Warner ten Kate, Adrienne Heinrich, Natallia Uzunbajakava, Gertruud Krekels, Denny Siem, and Gerard de Haan" Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis"IEEE journal of selected topics in signal processing, vol. 3, no. 1, february 2009
  28. JeffreyGlaister" Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness" IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 4, APRIL 2014.
  29. J. H. Morra, Z. Tu, L. G. Apostolova, A. E. Green, A. W. Toga, et al. , Comparison of AdaBoost and support vector machines for detecting Alzheimer's disease through automated Hippocampal segmentation, IEEE Transactions on Med- ical Imaging 29 (1) (2010) 30–43.
  30. E. Song, D. Huang, G. Maa, C. -C. Hung, Semi-supervised multi-class Adaboost by exploiting unlabeled data, Expert Systems with Applications 38 (6) (2011) 6720–600726.
  31. Z. -H. Zhou, M. -L. Zhang, Multi-instance multi-label learning with application to scene classification, in: Proceedings of the Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, 2006, pp. 1609–1616.
Index Terms

Computer Science
Information Sciences

Keywords

Classification image microscopy image mining intelligent planning skin cancer GLCM (Gray Level Co-occurrence matrix)