تعداد نشریات | 418 |
تعداد شمارهها | 9,995 |
تعداد مقالات | 83,546 |
تعداد مشاهده مقاله | 77,355,967 |
تعداد دریافت فایل اصل مقاله | 54,389,167 |
Using Genetic Algorithm for Optimization of Mammograms Image Compression | ||
Majlesi Journal of Electrical Engineering | ||
مقاله 2، دوره 4، شماره 3، آذر 2010، صفحه 7-18 اصل مقاله (554.47 K) | ||
نوع مقاله: Review Article | ||
شناسه دیجیتال (DOI): 10.1234/mjee.v4i3.222 | ||
نویسندگان | ||
Aynaz Besharat* ؛ Emad Fatemizadeh | ||
Sharif University of Technology | ||
چکیده | ||
In this study we created an optimized Region Of Interest (ROI) based JPEG2000 image compression algorithm for mammograms compression. The first step was to perform the standard JPEG2000 algorithm. The second step was to optimize this algorithm in different aspects which are, the type of wavelet transform, the number of decomposition levels of this transform and the quantization table for mammograms compression. Also we tried not to damage the diagnostic information in the images and keep the Peak Signal to Noise Ratio value, high. We achieved high compression ratios up to 165:1 with PSNR=47.96dB which was significantly higher than the previous results studied. At the next step we modified the optimized image compression algorithm in order to compress the mammograms with one square-shaped ROI in a way that we could compress the ROI losslessly. Therefore we could obtain a high total compression ratio and meanwhile preserve the significant medical diagnostic information. In previous studies on ROI-based 8bpp mammograms compression, the highest total CR for the ROI size of 5% and 15% of the entire image, with lossless ROI compression, were 32:1 and 12:1 respectively these values have been raised up to 49.9:1 and 21.33:1 in this study. | ||
کلیدواژهها | ||
Ms. in biomedical engineering؛ Mammograms Compression؛ en؛ JPEG2000؛ Optimization؛ Genetic Algorithm؛ Region of interest | ||
مراجع | ||
[1] Hun Yam Ch., Sari-Sarraf Hamed “Content-Based Compression of Mammograms with Customized Fractal Encoding and a modified JPEG2000”, Optical Engineering, Vol. 43, No. 12, p. 2986-2993, (2004)
[2] WU Y.G. “GA-based DCT Quantization Table Design Procedure for Medical Images” IEE proceedings. Vision, image and signal processing, Vol. 151, No. 5, pp. 353-359, (2004)
[3] Shoushtari,, P. “Compression of MRI Image based on Image features”, Sharif University of Technology, Technical Report, (September 2006).
[4] Chariloas Christopoulos, Touradj Ebrahimi; “JPEG2000 Still Image Coding System: An Over View”, IEEE Trans on Consumer Electronics, Vol. 46, No. 4, pp. 1103-1127, (November 2000)
[5] Christopoulos C., Askelof J.; Larsson M.; “Efficient Methods for Encoding Regions of Interest in the Upcoming JPEG2000 Still Image Coding Standard”, IEEE Signal Processing Letters, Vol. 7, No. 9, (September 2000)
[6] Khademi A., Krishnan S.; “Comparison of JPEG 2000 and Other Lossless Compression Schemes for Digital Mammograms”, Engineering in Medicine and Biology Society2005, IEEE-EMBS 27th Annual International Conference 2005, Vol. 17, No. 18, pp. 3771-3774, (January 2006)
[7] Chan H. Y., Sari-Sarraf H., Grinstead B., Gleason Sh. S.; “Content-based Compression of Mammograms with Fractal-based Segmentation and a Modified JPEG2000”, Optical Engineering, Vol. 43, No. 12, pp. 2986-2993, (2000)
[8] Grinstead B.; “Content-based Compression of mammograms”, Optical Engineering ,Texas Tech University, (May 2001)
[9] Gonzalez C., E. Woods Rafael, Richard L., Steven Eddins; Digital Image Processing, Using MATLAB, Pearson Educational. Inc, second edition, chapters 7 and 8, (2004)
[10] Christopoulos C., Askelof J., Larsson M. “Efficient Methods for Encoding Regions of Interest in the Upcoming JPEG2000 Still Image Coding Standard”, IEEE Signal Processing Letters, Vol. 7, No. 9, September (2000).
[11] Perlmutter S.M., Cosman P.C., Gray R.M., Olshen R.A., Ikeda D., Adams C.N., Betts B.J., Williams M., Perlmutter K.O., Li J., Aiyer A., Fajardo L., Birdwell R., Daniel B.L.; “Image Quality in Lossy Compressed Digital Mammograms”, Signal Processing, Special Section on Medical Image Compression, Vol. 59, No. 2, (June 1997)
[12] Zyout Imad, Abdolghader Iklas; “Progressive Lossy to Lossless Compression of ROI in Mammograms: Effects on microcalcification Detection”, Integrated Computer-Aided Engineering, Vol. 15, Issue 3, (2008)
[13] Said A., Pearlman W.A.; “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Trans on Circuits and Systems for Video Technology, Vol. 6 No. 3, pp. 243-25, (June 1996)
[14] Sung M.M., Kim H.J., Kim E.K., Kwak J.Y., Yoo,J.K; Yoo H.S.; “Clinical Evaluation of JPEG2000 Compression for Digital Mammography”, IEEE Trans on Nuclear Science June, Vol. 49, pp. 827-832, (2002)
[15] Lo SCB, Huai Li, Freedman M.T.; “Optimization Of Wavelet Decomposition for Image Compression and Feature Reservation” Medical Imaging IEEE Transactions, Vol. 22, No. 9, pp. 1141-1151, (September 2003)
[16] Yang Zeshang, Kallergi M., DeVore R.A., Lucier, B.J., Wei Qian, Clark R.A., Clarke L.P.; “Effect of Wavelet Bases on Compressing Digital Mammograms”, Engineering in Medicine and Biology Magazine IEEE, Vol. 14, No. 5, pp. 570-577, (September/October 1995)
[17] Grinstead Brad, Sari-Saraf Hamed, Shaun Gleason, Sunanda-Mitra; “Preliminary Validation of Content Based Compression of Mammographic Images”, Society of Photo-Optical Instrumentation Engineers, Image Processing Conference, Vol. 4322, (2001) | ||
آمار تعداد مشاهده مقاله: 78 تعداد دریافت فایل اصل مقاله: 30 |