Characterization of tissue from ultrasound images

The application of a procedure for classifying tissue types from unlabeled acoustic measurements using unsupervised analysis is reviewed and evaluated. Unsupervised learning techniques are applied to the problems of detecting tumors within an organ and of discriminating between tissue types of two neighboring organs such as the liver and the kidney.<<ETX>>

[1]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  T H Shawker,et al.  Quantitative estimation of liver attenuation and echogenicity: normal state versus diffuse liver disease. , 1987, Radiology.

[3]  L. Koran,et al.  The reliability of clinical methods, data and judgments (second of two parts). , 1975, The New England journal of medicine.

[4]  R. F. Wagner,et al.  Supervised Pattern Recognition Techniques In Quantitative Diagnostic Ultrasound , 1987, Other Conferences.

[5]  Michael F. Insana,et al.  Analysis of ultrasound image texture via generalized rician statistics , 1986 .

[6]  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.

[7]  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.

[8]  R. F. Wagner,et al.  Unified Approach to the Detection and Classification of Speckle Texture in Diagnostic Ultrasound , 1985, Optics & Photonics.

[9]  Robert F. Wagner,et al.  Progress In Signal And Texture Discrimination In Medical Imaging , 1985, Medical Imaging.

[10]  Murray H. Loew,et al.  Application Of Cluster Analysis And Unsupervised Learning To Multivariate Tissue Characterization , 1987, Other Conferences.

[11]  Michel Bertrand,et al.  Texture Analysis of Ultrasound B-Mode Images by Segmentation , 1984 .

[12]  E. Feleppa,et al.  Theoretical framework for spectrum analysis in ultrasonic tissue characterization. , 1983, The Journal of the Acoustical Society of America.

[13]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[14]  C. Metz,et al.  Statistical significance tests for binormal ROC curves , 1980 .

[15]  R. F. Wagner,et al.  Pattern Recognition Methods for Optimizing Multivariate Tissue Signatures in Diagnostic Ultrasound , 1986, Ultrasonic imaging.