Tissue classification with generalized spectrum parameters.

[1]  H. Tulinius,et al.  Tumours in Iceland. 3. Malignant tumours of kidney. A histological classification. , 2009, Acta pathologica et microbiologica Scandinavica. Section A, Pathology.

[2]  F. Forsberg,et al.  Tissue characterization using the continuous wavelet transform. II. Application on breast RF data , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[3]  B. Goldberg,et al.  Classification of ultrasonic B-mode images of breast masses using Nakagami distribution , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  J. Vaidya,et al.  Diseases of the breast , 2000, British Journal of Cancer.

[5]  L. Huang,et al.  Duct Detection and Wall Spacing Estimation in Breast Tissue , 2000, Ultrasonic imaging.

[6]  D. Chen,et al.  Breast cancer diagnosis using self-organizing map for sonography. , 2000, Ultrasound in medicine & biology.

[7]  Berkman Sahiner,et al.  Classification of malignant and benign masses based on hybrid ART2LDA approach , 1999, IEEE Transactions on Medical Imaging.

[8]  K. D. Donohue,et al.  Duct size detection and estimation in breast tissue , 1999, 1999 IEEE Ultrasonics Symposium. Proceedings. International Symposium (Cat. No.99CH37027).

[9]  Yung-Nien Sun,et al.  Ultrasonic image analysis for liver diagnosis , 1996 .

[10]  Tomy Varghese,et al.  Specular echo imaging with spectral correlation , 1995, 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium.

[11]  A. Stavros,et al.  Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. , 1995, Radiology.

[12]  K. D. Donohue,et al.  Estimating mean scatterer spacing with the frequency-smoothed spectral autocorrelation function , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  K. D. Donohue,et al.  Mean-scatterer spacing estimates with spectral correlation. , 1994, The Journal of the Acoustical Society of America.

[14]  Neil L. Gerr,et al.  The Generalised Spectrum and Spectral Coherence of a Harmonizable Time Series , 1994 .

[15]  Seong Ki Mun,et al.  Optimization of quantitative sonographic diagnostic analysis of breast lesions , 1994, Medical Imaging.

[16]  John S. DaPonte,et al.  Ultrasonic image texture classification using Markov random field models , 1994, Defense, Security, and Sensing.

[17]  J M Thijssen,et al.  Improvement of lesion detectability by speckle reduction filtering: a quantitative study. , 1993, Ultrasonic imaging.

[18]  R. L. Romijn,et al.  Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results. , 1991, Physics in medicine and biology.

[19]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.

[20]  Ó. Bjarnason,et al.  Tumours in Iceland 10. Malignant tumours of the female breast , 1988, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[21]  R. F. Wagner,et al.  Statistical properties of radio-frequency and envelope-detected signals with applications to medical ultrasound. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[22]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[23]  C R Hill,et al.  Tissue characterization from ultrasound B-scan data. , 1986, Ultrasound in medicine & biology.

[24]  Martin S. Roden.  analog and digital communication systems, 2nd. ed. , 1985 .

[25]  W. J. Lorenz,et al.  Diagnostic accuracy of computerized B‐scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease , 1985, Journal of clinical ultrasound : JCU.

[26]  P. Rosen.  The pathological classification of human mammary carcinoma: past, present and future. , 1979, Annals of clinical and laboratory science.

[27]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[28]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[29]  F. Forsberg,et al.  Analysis and classification of tissue with scatterer structure templates , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[30]  D. Koutsouris,et al.  PIO 7405 Efficacy of image texture analysis techniques in the classification of ultrasonic liver images , 1997 .

[31]  U Ranft,et al.  Random field models in the textural analysis of ultrasonic images of the liver , 1996, IEEE Trans. Medical Imaging.

[32]  E J Feleppa,et al.  Experimental assessment of spectrum analysis of ultrasonic echoes as a method for estimating scatterer properties. , 1994, Ultrasound in medicine & biology.

[33]  J M Thijssen,et al.  Detection of diffuse liver disease by quantitative echography: dependence on a Priori choice of parameters. , 1993, Ultrasound in medicine & biology.

[34]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.

[35]  R. L. Romijn,et al.  Ultrasonic differentiation of intraocular melanomas: parameters and estimation methods. , 1991, Ultrasonic imaging.

[36]  E. Feleppa,et al.  Relationship of Ultrasonic Spectral Parameters to Features of Tissue Microstructure , 1987, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[37]  Martin S. Roden.  Analog and Digital Communication Systems , 1979 .

[38]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .