||A Novel Hardware Implementation of Wavelet-based Octave Energy for Breast Sonogram Tumor Characterization
||Department of Electrical Engineering
Wavelet-Based Octave Energy
Breast Cancer Classification
Segment Accumulation Algorithm (SAA)
Reversible Round-off 1-D Non-Recursive DPWT
The infiltrative nature of lesions is a significant feature that implies a malignant breast lesion in ultrasound images. Characterizing the infiltrative nature of lesions with computationally inexpensive and highly efficacious features is crucial for the realization of a computer-aided diagnosis system. In this study, the infiltrative nature of lesions is regarded as an energy that produces irregular and considerably local variances in a 1-D signal. The local variances can be characterized by a few high octave energies (i.e., the channel energies close to low frequency bands) in 1-D discrete periodized wavelet transform (DPWT). To reduce computation cost, high octave decomposition is performed by a reversible round-off 1-D non-recursive DPWT (1-D RRO-NRDPWT).
For breast lesion classification, contour features should be able to resist noise and contour variation. To evaluate the differences in performance and feature value induced by the variations in boundary definitions, two delineation methods were considered, hand-painted and semi-automatic segmentation. A high individual performance result implies that the proposed feature is well correlated with radiologist’s perception and closer to match those in trained physician than morphometric parameters. The performance differences in the three ImageJ-generated datasets derived by variant setting parameters are not significant. Experimental results also reveal that the proposed feature is suitable for combination with some morphometric parameters for performance improvement.
For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented in this dissertation. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images.
List of Tables viii
List of Figures x
CHAPTER 1 Introduction 1
1.1 Background and Motivation 1
1.2 Organization of the Dissertation 5
CHAPTER 2 The Reversible Round-Off Non-Recursive 1-D Discrete Periodized Wavelet Transform 6
2.1 Exploration of Word-Length-Growth Effect. 7
2.2 The Non-Recursive 1-D Discrete Periodized Wavelet Transform 7
2.3 The Reversible Round-Off 1-D NRDPWT Theorem 12
2.4 Finite Precision Performance of the Reversible Round-Off 1-D NRDPWT 19
CHAPTER 3 Octave Energy Features for Breast Lesion Description 23
3.1 Breast US Images Acquisition 24
3.2 Derivation of Breast Lesion Contour 24
3.3 Resampling Process 29
3.4 Multiresolution Representations of Breast Lesion Shapes 30
3.5 Measurement of Fisher’s Estimation 33
CHAPTER 4 Experimental Results of Classification Performance Analysis 35
4.1 Materials and Methods 35
4.2 Experimental Results 36
4.2.1 Individual performance of morphometric and octave energy features by using manual delineated breast lesions 36
4.2.2 Combined performance of morphometric and octave energy features by using manual delineated breast lesions 39
4.2.3 Performance of morphometric and octave energy features by using semi-automatic delineated breast lesions 42
CHAPTER 5 Segment Accumulation Algorithm for the Realization of High Octave Decomposition 50
5.1 The Segment Accumulation Algorithm 50
5.2 Example 53
CHAPTER 6 SAA-Based VLSI Architecture for High Octave Decomposition 56
6.1 VLSI Architecture Design of Segment Accumulation Algorithm 56
6.2 Bit Performance Analysis of NRDPWT Filter Coefficients 60
6.3 Hardware Simulation Results 67
CHAPTER 7 Discussions and Conclusions 72
Publication List 87
 A. T. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology, vol. 196, pp. 123-134, July 1995.
 P. Skaane, “Ultrasonography as adjunct to Mammography in the evaluation of breast tumors,” Acta Radiologica Supplementum, vol. 40, suppl. 000, pp. 1-47, 1999.
 T. M. Kolb, J. Lichy, and J. H. Newhouse, “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: An analysis of 27825 patient evaluations,” Radiology, vol. 225, pp. 165-175, Oct. 2002.
 W. Leucht and D. Leucht, Teaching Atlas of Breast Ultrasound. New York: Thieme Medical, 2000, pp. 24-38.
 K. J. W. Taylor, C. Merritt, C. Piccoli, R. Schmidt, G. Rouse, B. Fornage, E. Rubin, D. Georgian-Smith, F. Winsberg, B. Goldberg, and E. Mendelson., “Ultrasound as a complement to mammography and breast examination to characterize breast masses,” Ultrasound Med. Biol., vol. 28, pp.19-26, Jan. 2002.
 S. Gefen, O. J. Tretiak, C. W. Piccoli, K. D. Donohue, A. P. Petropulu, P. M. Shankar, V. A. Dumane, L. Huang, M. A. Kutay, V. Genis, F. Forsberg. J. M. Reid, and B. B. Goldberg, “ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis,” IEEE Trans. Med. Imag., vol. 22, pp. 170-177, Feb. 2003.
 Y. Zheng, J. F. Greenleaf, and J. J. Gisvold, “Reduction of breast biopsies with a modified self-organizing map,” IEEE Trans. Neural Networks, vol. 8, pp.1386-1396, Nov. 1997.
 D. R. Chen, R. F. Chang, W. J. Kuo, M. C. Chen, and Y. L. Huang, “Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks,” Ultrasound Med. Biol., vol. 28, pp.1301-1310, Oct. 2002.
 V. Goldberg, A. Manduca, D. J. Ewert, J. J. Gisvold, and J. F. Greenleaf, “Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence,” Med. Phys. Vol. 19, pp. 1475-1481, Nov. 1992.
 Y. L. Huang, D. R. Chen, and Y. K. Liu, “Breast cancer diagnosis using image retrieval for different ultrasonic systems,” in Proc. Int. Conf. Image Processing, Oct. 2004, pp. 2957-2960.
 X. Cheng, K. Itoh, A. Ohya, K. Omoto, Y. Wang, N. Taniguchi, S. Ogawa, and I. Akiyama, “Intelligent systems using fuzzy logic for the determination of breast tumors,” in Proc. 20th Annual Int. Conf. the IEEE Eng. in Med. and Biol., Oct. 1998, pp.1356-1359.
 C. M. Chen, Y. H. Chou, K. C. Han, G. S. Hung, C. M. Tiu, H. J. Chiou, and S. Y. Chiou, “Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks,” Radiology, vol. 226, pp.504-514, Feb. 2003.
 A. V. Alvarenga, W. C. A. Pereira, A. F. C. Infantosi, and C. M. de Azevedo, “Classification of breast tumours on ultrasound images using morphometric parameters,” in Proc. IEEE Int. Symp. on Intell. Signal Processing, Sept. 2005, pp. 206-210.
 M. Giger, “Computer-aided diagnosis of breast lesions in medical images,” Comput. Med., vol. 2, pp.39-45, Sept. 2000.
 A. Madabhushi and D. N. Metaxas, “Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions,” IEEE Trans. Med. Imag., vol. 22, pp. 155-169, Feb. 2003.
 S. F. Huang, R. F. Chang, W. K. Moon, Y. H. Lee, D. R. Chen, and J. S. Suri, “Analysis of tumor vascularity using three-dimensional power doppler ultrasound images,” IEEE Trans. Medical Imaging, vol. 27, pp. 320-330, Mar. 2008.
 C. M. Sehgal, P. H. Arger, S. E. Rowling, E. F. Conant, C. Reynolds, and J. A. Patton, “Quantitative vascularity of breast masses by doppler imaging: regional variations and diagnostic implications,” J. of Ultrasound Med., vol. 19, pp. 427-440, July 2000.
 C. K. Abbey, R. J. Zemp, J. Liu, K. K. Lindfors, and M. F. Insana, “Observer efficiency in discrimination tasks simulating malignant and benign breast lesions imaged with ultrasound,” IEEE Trans. Med. Imag., vol. 25, pp. 198-209, Feb. 2006.
 P. M. Shankar, V. A. Dumane, J. M. Reid, V. Genis, F. Forsberg, C. W. Piccoli, and B. B. Goldberg, “Classification of ultrasonic B-mode images of breast masses using the Nakagami distribution,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. 48, pp. 569–580, Mar. 2001.
 Breast Imaging Reporting and Data System Atlas. Reston, VA: American College of Radiology, 2003.
 K. Drukker, N. P. Gruszauskas, C. A. Sennett, and M. L. Giger, “Breast US computer-aided diagnosis workstation: performance with a large clinical diagnostic population,” Radiology, vol. 248, pp. 392-397, Aug. 2008.
 Y. L. Huang and D. R. Chen, "Automatic Contouring for Breast Tumors in 2-D Sonography," in Proc. IEEE Int. Conf. Medicine and Biology, Sept. 2005, pp. 3225-3228.
 A. V. Alvarenga, A. F. C. Infantosi, C. M. Azevedo, W. C. A. Pereira, “Application of morphological operators on the segmentation and contour detection of ultrasound breast images ”, Brazilian J. of Biomed. Eng., vol. 19, pp. 91-101. Aug. 2003.
 R. F. Chang, S. F. Huang, W. K. Moon, Y. H. Lee, and D. R. Chen, “Solid breast masses: neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification,” Radiology, vol. 243, pp. 56-62, Apr. 2007.
 K. K. Parhi, and T. Nishitani, “VLSI architectures for discrete wavelet transforms,” IEEE Trans. VLSI Syst., vol. 1, pp. 191-202, June 1993.
 C. Chakrabarti, and M. Vishwanath, “Efficient realizations of the discrete and continuous wavelet transforms: from single chip implementations to mappings on SIMD array computers,” IEEE Trans. Sig. Processing, vol. 43, pp. 759-771, Mar. 1995.
 C. Chakrabarti, M. Vishwanath, and R. M. Owens, “Architectures for wavelet transforms: a survey,” J. VLSI Sig. Proc., vol. 14, pp. 171-192, Nov. 1996.
 M. Vishwanath, R. M. Owens, and M. J. Irwin, “VLSI architectures for the discrete wavelet transform,” IEEE Trans. Circuits and Systems, vol. 42, pp. 305-316, May 1995.
 A. Grzeszczak, M. K. Mandal, and S. Panchanathan, “VLSI implementation of discrete wavelet transform,” IEEE Trans. VLSI Systems, vol. 4, pp. 421-433, Dec. 1996.
 J. C. Limqueco and M. A. Bayoumi, “A VLSI architecture for separable 2-D discrete wavelet transform,” J. VLSI Signal Processing, vol. 18, pp. 125-140, Feb. 1998.
 G. Lafruit, F. Catthoor, J. P. H. Cornelis, and H. J. D. Man, “An efficient VLSI architecture for 2D wavelet image coding with novel image scan,” IEEE Trans. VLSI Systems, vol. 7, pp. 105-110, March 1999.
 T. C. Denk and K. K. Parhi, “Systolic VLSI architectures for 1-D discrete wavelet transforms,” in Proc. Asilomar Conf. on Signals, Systems and Computers, Nov. 1998, pp. 1220-1224
 C. G. Zhang, C. Y. Wang, and M. O. Ahmad, “A VLSI architecture for a high-speed computation of the 1D discrete wavelet transform,” in Proc. IEEE Int. Symp. Circuits Syst., May 2005, pp. 1461-1464.
 C. G. Zhang, C. Y. Wang, and M. O. Ahmad, “An efficient buffer-based architecture for on-line computation of 1-D discrete wavelet transform,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, May 2004, pp. 201-204.
 F. Marino, D. Guevorkian, and J. T. Astola, “Highly efficient high-speed/low-power architectures for the 1-D discrete wavelet transform,” IEEE Trans. Circuits Syst. II, vol.47, pp.1492-1502, Dec. 2000.
 J. M. Jou, Y. H. Shiau, and C. C. Liu, “Efficient VLSI architectures for the biorthogonal wavelet transform by filter bank and lifting scheme,” in Proc. IEEE Int. Symp. Circuits Syst., May 2001, pp. 529-532.
 K. Andra, C. Chakrabarti, and T. Acharya, “A VLSI architecture for lifting-based forward and inverse wavelet transform,” IEEE Trans. Signal Processing, vol. 50, pp.966-977, Apr. 2002.
 P. Y. Chen, “VLSI implementation for one-dimensional multilevel lifting-based wavelet transform,” IEEE Trans. Computers, vol. 53, pp. 386-398, Apr. 2004.
 W. Jiang and A. Ortega, “Efficient discrete wavelet transform architectures based on filterbank factorizations,” in Proc. Int. Conf. Image Processing, Feb. 1999, pp. 749-753.
 H. Liao, M. K. Mandal, and B. F. Cockburn, "Efficient architectures for 1-D and 2-D lifting-based wavelet transforms," IEEE Trans. Signal Processing, Vol. 52, pp. 1315-1326, May 2004.
 K. Oweiss, A. Mason, K. Thomson, Y. Suhail, A. Kamboh, “A scalable wavelet transform VLSI architecture for real-time neural signal conditioning in implantable multichannel neuroprosthetic devices,” IEEE Trans. Circuits and Systems I, vol. 54, pp. 1266-1278, June 2007.
 C. T. Huang, P. C. Tseng, and L. G. Chen, “VLSI architecture for discrete wavelet transform based on B-spline factorization,” in Proc. IEEE Workshop Sig. Proc. Syst., Aug. 2003, pp. 346-350.
 C. T. Huang, P. C. Tseng, and L. G. Chen, “B-spline factorization-based architecture for inverse discrete wavelet transform,” in Proc. IEEE Int. Symp. Circuits Syst., May 2004, pp. 829-832.
 C. T. Huang, P. C. Tseng, and L. G. Chen, “VLSI architecture for forward discrete wavelet transform based on B-spline factorization,” J. VLSI Signal Processing Systems, vol. 40, pp. 343-353, Jul. 2005.
 A. M. Kamboh, K. G. Oweiss, A. Mason, “Analysis of lifting and B-spline DWT implementations for implantable neuroprosthetics,” J. VLSI Signal Processing, vol. 52, pp. 249-261, Sept. 2008.
 C. T. Huang, P. C. Tseng, and L. G. Chen, “Flipping structure: an efficient VLSI architecture for lifting-based discrete wavelet transform,” in Proc. IEEE Asia-Pacific Conf. Circuits and Syst., Dec. 2002, pp. 383-388.
 C. T. Huang, P. C. Tseng, and L. G. Chen, “Flipping Structure: an efficient VLSI architecture for lifting-based discrete wavelet transform,” IEEE Trans. Signal Processing, vol. 52, pp. 1080-1089, Apr. 2004.
 W. Sweldens, “The lifting scheme: A custom-design construction of biorthogonal wavelets,” Appl. Comput. Harmon. Anal., vol. 3, pp. 186-200, 1996.
 I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” J. Fourier Anal. Appl., vol. 4, pp. 247–269, 1998.
 M. Unser and T. Blu, “Wavelet theory demystified,” IEEE Transactions Signal Processing, vol. 51, pp. 470–483, Feb. 2003.
 S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 674-693, July 1989.
 H. Choi, W. P. Burleson, and D. S. Phatak, “Optimal wordlength assignment for the discrete wavelet transform in VLSI,” in Proc. IEEE Workshop VLSI Signal Processing, Oct. 1993, pp. 325-333.
 L. Wanhammar, DSP Integrated Circuit. San Diego, Calif.: Academic Press, 1999.
 A. Benkrid, K. Benkrid, and D. Crookes, “A novel approach for diminishing and predicting the error dynamic range in finite wordlength FIR based architectures,” in Proc. IEEE Int. Conf. Acoustic, Speech, and Sig. Processing, April 2003, pp.581-584.
 C. T. Ku, H. S. Wang, K. C. Hung, and Y. S. Hung, “High efficient ECG compression based on reversible round-off non-recursive 1-D discrete periodized wavelet transform,” Med. Engineering Phys. , vol. 29, pp. 1149-1166, Dec. 2007.
 C. F. Tsai, H. S. Wang, K. C. Hung, “A high efficient non-recursive discrete periodized wavelet transform for extracting the transformed coefficients of coarser resolution levels,” in Proc. IEEE Asia-Pacific Conf. Circuits and Syst., Dec. 2004, pp. 661-664.
 C. T. Ku, H. S. Wang, K. C. Hung, and Y. S. Hung, “A novel ECG data compression method based on nonrecursive discrete periodized wavelet transform,” IEEE Trans. Biomed. Engineering, vol. 53, pp. 2577-2583, Nov. 2006.
 C. T. Ku, K. C. Hung, and H. S. Wang, “A high efficient quality control strategy for wavelet-based ECG data compression system,” in Proc. Int. Conf. Biomed. Engineering and Informatics, May 2008, pp. 320-323.
 K. C. Hung, Y. S. Hung, and Y. J. Huang, “A nonseparable VLSI architecture for two-dimensional discrete periodized wavelet transform,” IEEE Trans. VLSI Systems, vol.9, pp.565-576, Oct. 2001.
 A. S. Lewis and G. Knowles, “VLSI architecture for 2D Daubechies wavelet transform without multipliers,” Electron Lett., vol.27, pp.171-173, Jan. 1991.
 I. Daubechies, Ten Lectures on Wavelet. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 1992.
 K. C. Hung, “The generalized uniqueness wavelet descriptor for planar closed curves”, IEEE Trans. Image Processing, vol. 9, pp. 834-845, May 2000.
 G. C. H. Chuang and C. C. J. Kuo, “Wavelet descriptor of planar curves: theory and applications,” IEEE Trans. Image Processing, vol. 5, pp. 56-70, Jan. 1996.
 [Online]. Available: http://rsb.info.nih.gov/ij/index.html
 [Online]. Available: http://ImageJdocu.tudor.lu/Members/tboudier/plonearticle.2006-07-12.1260738650/
 M. Nadler and E. P. Smith, Pattern Recognition Engineering. New York: Wiley-Interscience, 1993.
 K. K. Parhi, VLSI Digital Signal Processing Systems: Design and Implementation. New York :Wiley, 1999.