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Content Based Image Retrieval (CBIR) System for Diagnosis of Blood Related Diseases

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IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013
© 2013 by IJCA Journal
NCIPET2013 - Number 1
Year of Publication: 2013
Authors:
Jignyasa Sanghavi
Deepali Kayande

Jignyasa Sanghavi and Deepali Kayande. Article: Content Based Image Retrieval (CBIR) System for Diagnosis of Blood Related Diseases. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013 NCIPET 2013(1):11-15, December 2013. Full text available. BibTeX

@article{key:article,
	author = {Jignyasa Sanghavi and Deepali Kayande},
	title = {Article: Content Based Image Retrieval (CBIR) System for Diagnosis of Blood Related Diseases},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013},
	year = {2013},
	volume = {NCIPET 2013},
	number = {1},
	pages = {11-15},
	month = {December},
	note = {Full text available}
}

Abstract

The identification of the disease is very crucial step for curing disease. In many cases microscopic analysis of peripheral blood samples by medical practitioner is an important test in the procedures for the diagnosis of any blood related disease. Accurate diagnosis of disease is crucial for curing and controlling that disease. Earlier this process was carried out only by medical experts but now a day's automated system based on computer vision methods or image processing algorithms can speed up this operation. The expert systems are developed which are doing computerized diagnosis of various diseases using digital images of blood samples. Digital images are acquired using a digital camera connected to microscope. The presented paper shows the automatic diagnosis for three diseases i. e. Leukemia, Malaria and Sickle cell Anaemia. The system firstly segments the infected cells of leukemia and sickle cell anaemia or parasites of malaria from the blood samples and extracts the features of these cells or parasites. These features are then compared with database and accordingly classification is done and is represented in CBIR (Content Based Image Retrieval) framework.

References

  • World Health Organization. What is Malaria? and What is Leukemia? http://apps. who. int/gb/ebwha/pdf_files/EB118/B118_5-en. pdf.
  • R. Labati,et al, "ALL-IDB:The Acute Lymphoblastic Leukemia Image Database for image processing",IEEE conference on Image Processing,2011.
  • C. Reta,et al, "Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute Leukemia",Proceedings of 23rd International Florida Artificial Intelligence conference,2010.
  • F. Scotti, "Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images", IMTC conference Italy, 2006.
  • F. Scotti, "Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images", IEEE conference on Computational Intelligence,2005.
  • D. Foran, et al, "Computer –Assisted Discrimination Among Malignant Lymphomas and Leukemia Using Immunophenotyping,Intelligent Image Repositories and Telemicroscopy",IEEE Transactions on Information Technology in Biomedicine,vol 4,2000.
  • D. Anggraini, et al, "Automated Status Identification of Microscopic Images obtained from Malaria Thin Blood Smears" IEEE conference Indonesia,2011.
  • J. Soni, "Advanced Image Analysis based system for automatic Detection of Malarial Parasite in Blood Images using SUSAN approach" IJEST Vol. 3 No. 6, 2011.
  • Y. Hirimutugoda, et al, "Image Analysis System for Detection of Red Cell Disorders using Artificial Neural Network" Sri Lanka Journal of Bio-Medical Informatics,2010.
  • F. Boray Tek, et al, "Computer vision for microscopy diagnosis of malaria" Malaria Journal 2009.
  • F. Boray Tek, et al, "Malaria Parasite Detection in Peripheral Blood Images" Proceedings of Medical Image understanding and Analysis conference,2006.
  • J. Gim, et al, "A novel framework for white blood cell segmentation based on stepwise rules and morphological features",Proceedings of SPIE,2011.
  • B. Ko, et al, "Microscopic cell Nuclei Segmentation Based on Adaptive Attention Window" Proceedings of Journal of Digital Imaging vol 22,June,2009.
  • F. sadeghian, et al, "A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing",Proceedings of Biological Procedures Online,vol 11,2009.
  • J. Wang , et al, "Pathfinder: Multiresolution Region-Based Searching of Pathology Images using IRM",Proceedings of the AMIA Symposium,2000
  • C. Carson, et al, "Blobworld: A System for Region Based Image Indexing and Retrieval ",Springer,Visual Information Systems,1999.
  • Rafeal C. Gonzalez,Richard E. Woods,Digital Image Processing,2nd Edition,Prentice Hall,2006.
  • V. Piuri and Fabio Scotti, "Morphological Classification of Blood Leucocytes by Microscope Images" IEEE conference USA,2004.
  • D. Comaniciu et al, "Bimodal System for Interactive Indexing and Retrieval of Pathology Images",0-8186-8606-5/98 IEEE,1998.
  • D. Comaniciu et al, "Shape-Based Image Indexing and Retrieval for Diagnostic Pathology", IEEE conference on Pattern Recognition,1998.
  • L. Dorini et al, "White blood cell segmentation using morphological operators and scale-space analysis", IEEE conference on Computer Graphics and Image Segmentation,2007.
  • Lei Zheng et al, "Design and Analysis of a Content-Based Pathology Image Retrieval System"IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 4, December 2003.
  • www. netofcare. org/content/pdf/6-spec_illness-sicklecell. pdf
  • http://dti. unimi. it/fscotti/all
  • http://dpd. cdc. gov/DPDx/HTML/ImageLibrary/Malaria_il. htm
  • http://www. emedicinehealth. com/leukemia/article_em. htm
  • J. Sanghavi et al,"Morphological Classification of few blood related disease using peripheral Blood microscopic images"Proceeding of ITAC,2012.
  • Topi Maenpaa & Matti Pietikainen,"Texture Analysis with Local Binary Patterns", WSPC Review volume, May 13, 2004.
  • T. Lehmann, B. Wein, J. Dahmen, J. Bredno, F. Vogelsang and M. Kohnen, "Content-Based Image Retrieval in Medical Applications : A Novel Multi-Step Approach", In International Society for Optical Engineering (SPIE), volume 3972(32), pages.
  • A. Kak and C. Pavlopoulou. "Content-Based Image Retrieval from Large Medical Databases. In 3D Data Processing," Visualization, Transmission, Padova, Italy, June 2002
  • F. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas, "Fast and effective retrieval of medical tumor shapes". IEEE Trans. on Knowledge and Data Engineering,10(6):889–904, 1998.
  • H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler. "A review of content-based image retrieval systems in medical applications – clinical benefits and future directions. " International Journal of Medical Informatics, 73(1):1{23, Feb. 2004.
  • H. D. Tagare, C. C. Ja_e, and J. Duncan. Medical Image Databases: " A Content-based Retrieval Approach". J Am Med Inform Assoc, 4(3):184,198,1997.