International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 3, No. 5, 8 Oct. 2011
Cover page and Table of Contents: PDF (size: 344KB)
A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework
Full Text (PDF, 344KB), PP.33-39
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Author(s)
Ping Xu
1,*
Yan Zuo
2
Wei-Dong Xu
1
Hua-Jie Chen
2
Index Terms
Digital mammography, diagnosis lossless compression, CAD, ROI
Abstract
With the rapidly growing use of digital images in medical archival and communication, image compression technology, especially diagnosis lossless compression technology, plays a more and more important role for medical applications. In this thesis, a novel diagnosis loseless compression algorithm is presented for digital mammography. The mammogram is divided into breast region, pectoral muscle and background using the CAD technology. Then mutiple arbitrary shape ROIs coding framework is used to compress the mammogram in which the breast region and pectoral muscle are compressed losslessly and lossily respectively, and the background can be discarded or compressed lossily as user’s will. Experimental results show that the proposed method offer potential advantage in medical applications of digital mammography compression.
Cite This Paper
Ping Xu, Yan Zuo, Wei-Dong Xu, Hua-Jie Chen, "A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework", International Journal of Modern Education and Computer Science(IJMECS), vol.3, no.5, pp.33-39, 2011. DOI:10.5815/ijmecs.2011.05.05
Reference
[1]R. G. Bird, T. W. Wallace, and B. C. Yankaskas, Analysis of cancers missed at screening mammography, Radiology, vol. 184, pp. 613–617,1992.
[2]H. Burhenne, L. Burhenne, F. Goldberg, T. Hislop, A. J. Worth, P. M.Rebbeck, and L. Kan, Interval breast cancers in the screening mammographyprogram of British Columbia: Analysis and classification, Am. J. Roentgenol., vol. 162, pp. 1067–1071, 1994.
[3]K. Doi, H. MacMahon, S. Katsuragawa, R. M. Nishikawa, and Y. Jiang, Computer-aided diagnosis in radiology: Potential and pitfall, Eur. J.Radiol., vol. 31, pp. 97–109, 1999.
[4]H. Li, K. J. Liu, and S. Lo, Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms, IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 785–798, Dec. 1997.
[5]Bradley J. Erickson M D, Irreversible Compression of Medical Images, J. Digit. Imag., 15(1): 5-14,2002.
[6]Ishigaki T, Sakuma S, Ikeda M et al, Clinical evaluation of irreversible image compression: Analysis of chest imaging with computed radiography, J. Radiol., 175:739–743, 1990.
[7]Slone R M, Foos D H, Whiting B R et al, Assessment of visually lossless irreversible image compression: Comparison of three methods by using an image comparison workstation, Radiology-Computer Applications, 215(2):543–553, 2000.
[8]Penedo M, Pearlman W A, Tahoces P G et al, Region-based wavelet coding methods for digital mammography. IEEE Trans Med Imaging, 22:1288–1296, 2003.
[9]Good W F, Sumkin J H, Ganott M et al, Detection of masses and clustered microcalcifications on data compressed mammograms: an observer performance study. AJR Am J Roentgenol, 175:1573–1576, 2000.
[10]Zheng B, Sumkin J H, Good W F et al, Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: an assessment. Acad Radiol, 7:595–602, 2000.
[11]Kocsis O, Costaridou L, Varaki L et al, Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms. Eur Radiol, 13: 2390–2396, 2003.
[12]Perlmutter S, Cosman P, Gray Ret al, Image quality in lossy compressed digital mammograms. Signal Process,59:189–210,1997.
[13]Sung M M, Kim H J, Kim E K et al, Clinical evaluation of JPEG2000 compression for digital mammography. IEEE Trans Nucl Sci, 49: 827–832, 2002.
[14]Suryanarayanan S, Karellas A, Vedantham S et al, A perceptual evaluation of JPEG 2000 image compression for digital mammography: contrast detail characteristics. J Digit Imaging, 17:64–70, 2004.
[15]Penedo M, Carreira J M, Tahoces P G et al, Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms, IEEE Trans Inf Technol Biomed, 10(2):354–361, 2006.
[16]Suryanarayanan S, Karellas A, Vedantham S et al, Detection of Simulated Lesions on Data compressed Digital Mammograms, Radiology, 236:31–36, 2005.
[17]Idris F M, AlZubaidi N I, Detection of breast cancer in the JPEG2000 domain, Trans Eng, Comput Technol, 8:1305-5313, 2005.
[18]Penedo M, Souto M, Tahoces P G et al, FROC evaluation of JPEG2000 and object-based SPIHT lossy compression on digitized mammograms, Radiology, 237: 450–457, 2005.
[19]Ahmed Abu-Hajar and Ravi Sankar, “Integer-to-integer shape adaptive wavelet transform for region of interest image coding”, Digital Signal Processing Workshop, pp:94 – 97, Oct. 2002.
[20]Zhongmin Liu et al., “Cascaded differential and wavelet compression of chromosome images”, IEEE Transaction on Biomedical Engineering, 49(4),pp. 372-383, Apr. 2002.
[21]Xu W, Study on computer-aided diagnosis of ammograms, Ph.D. dissertation, Dept. Biomedical Engineering, Zhejiang Univ, Hangzhou, China, 2006. (in Chinese)
[22]Xu W, Xia S, A model based algorithm to segment the pectoral muscle in mammograms, in proc IEEE Int. Neural Networks & Signal Processing Conf, pp.1163-1169, Dec 2003.