Representasi Kode IRMA pada Basis Data Mammografi MIAS

Karmilasari Karmilasari, Suryarini Widodo, Lussiana ETP

Abstract


Limitations of mammography database with image coding and the identification of a variety of
characteristics, such as pathology, and abnormal breast tissue types, is an issue in the development of
computer systems for the diagnosis of breast cancer. IRMA coding system was developed to facilitate
content-based image retrieval identify (CBIR) as a prototype application in medical diagnostic radiology
imagery. IRMA Code was developed following the network code American College of Radiology (ACR)
and data system (BI-RAD). Through IRMA code, obtained standardized code for the type of tissue, the
level of tumor and lesion description. The results of the code in the form of a character string of no more
than 13 characters (IRMA: YYYY - DDD - AAA - BBB). The code can be extended by introducing
characters in certain positions code if there is a new modality is introduced. IRMA coding system can be
applied to mammographic Digital Mammogram Image Analysis Society (MIAS). Complete initial
information from mammography is the basis for the study of medical image breast cancer, while the final
information obtained from IRMA coding system can be input for clinicians in decision-making for patient
action.
Keywords : Mammography, IRMA coding system, MIAS database

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References


American College of Radiology. Breast Imaging Reporting and Data System (BI-RADS©). Atlas.

Burhenne LJW, Wood SA, D’Orsi CJ, Feig SA, Kopans DB, Castellino RA. “Potential contribution of

computer aided detection to the sensitivity of screening mammography”. Radiol. 2000; 215: 554-

Christoyianni I, Dermantas E, Kokkinakis G. “Automatic detection of abnormal tissue in

mammography”. Proceedings ICIP. 2001; 877-80.

Deselaers T, Müller H, Clough P, Ney H, Lehmann TM. “The CLEF 2005 automatic medical image

annotation task”. Intl J Comp Vis. 2007; 74(1): 51-8.

Elter M, Schulz-Wendtland R, Wittenberg T. “The prediction of breast cancer biopsy outcomes using

two CAD approaches that both emphasize an intelligible decision process”. Med Phys. 2007;

(11): 4164-72.

Lehmann TM, Güld MO, Deselaers T, Keysers D, Schubert H, Spitzer K, Ney H, Wein BB.”

Automatic categorization of medical images for content-based retrieval and data mining”. Comput

Med Imaging Graph. 2005; 29(2): 143-155.

Lehmann TM, Güld MO, Thies C, Fischer B, Spitzer K, Keysers D, Ney H, Kohnen M, Schubert H,

Wein BB. ”Content-based image retrieval in medical applications”. Methods Inform Med. 2004;

(4): 354-61.

Lehmann TM, Schubert H, Keysers D, Kohnen M, Wein BB. “The IRMA code for unique

classification of medical images”. Proc SPIE. 2003; 5033:440-51.

Muller H, Michoux N, Bandon D, Geissbuhler A. “A review of content-based image retrieval systems

in medical applications : Clinical benefits and future directions”. Int J Med Inform. 2004; 73(1): 1-23.

Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. “Content-based image retrieval at the end

of the early years”. IEEE Trans Pattern Anal Mach Intell. 2000; 22(12): 1349-80.

Suckling J, et al. “The Mammographic image analysis society digital mammogram database”,

Exerpta Medica International Congress. 1994; 1069: 375-8.

World Health Organization. GLOBOCAN 2008. International Agency for Research on Cancer. 2008.

Zwiggelaar R, Astley SM, Boggis CRM, Taylor CJ. “Linear structures in mammographic images:

detection and classification”. IEEE Trans Med Imaging. 2004; 23(9): 1077-86.


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