Computer construction and presentation of 3D images

1984 ◽  
Vol 42 ◽  
pp. 546-549
Author(s):  
C. N. Liu
Keyword(s):  
Image Processing ◽  
Ultrasonic Attenuation ◽  
Three Dimensional ◽  
Processing System ◽  
Ultrasonic Tomography ◽  
Computer Algorithms ◽  
Dimensional Image ◽  
Dimensional Reconstruction ◽  
Ultrasonic Images ◽  
Transmission Scans

Three-dimensional reconstruction is a subject of considerable importance in many scientific and engineering investigations. This study is concerned with the development of new computer techniques and system by which the structures of an object are assembled for precise interpretation from their two-dimensional ultrasonic images. This paper describes a computer controlled ultrasonic scanning and data acquisition system, a set of computer algorithms for reconstructing ultrasonic attenuation from transmission scans, i.e. computerized ultrasonic tomography, and an image processing system for three-dimensional image processing and presentation. The above systems and techniques are illustrated using a simple phantom and an example of reconstructed three-dimensional image of this phantom in terms of ultasonic attenuation is presented.

Three-Dimensional Image Processing System for the Ureter and Urethra Using Endoscopic Video

2008 ◽  
Vol 22 (8) ◽  
pp. 1569-1572 ◽  
Author(s):  
Tatsuo Igarashi ◽  
Satoki Zenbutsu ◽  
Tomonori Yamanishi ◽  
Yukio Naya
Keyword(s):  
Image Processing ◽  
Three Dimensional ◽  
Processing System ◽  
Image Processing System ◽  
Dimensional Image ◽  
Endoscopic Video

scholarly journals Development of High Speed and High Quality Three-dimensional Image Processing System for Helical CT

1995 ◽  
Vol 51 (8) ◽  
pp. 940
Author(s):  
Yuko Tanaka ◽  
Norihiro Gunji ◽  
Ichiro Yamagishi ◽  
Tomoyasu Komori ◽  
Tomohiko Kihara ◽  
...  
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Three Dimensional ◽  
Processing System ◽  
Helical Ct ◽  
High Quality ◽  
Image Processing System ◽  
Dimensional Image

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

SEGMENTATION OF ULTRASONIC IMAGES WITH NEURAL NETWORKS

1991 ◽  
Vol 05 (04) ◽  
pp. 619-628 ◽  
Author(s):  
RONALD H. SILVERMAN
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Back Propagation ◽  
Three Dimensional ◽  
Competitive Learning ◽  
Data Sets ◽  
Image Structure ◽  
Ultrasonic Images ◽  
Traditional Approaches

Neural networks differ from traditional approaches to image processing in terms of their ability to adapt to regularities in image structure and to self-organize so as to implement directed transformations. Biomedical ultrasonic images are often degraded in quality by noise and other factors, making enhancement techniques particularly important. This paper describes use of back propagation and competitive learning for enhancement and segmentation of ultrasonic images of the eye. Of particular interest is the extension or these technique to segmentation of three-dimensional data sets, where simple thresholding and gradient operations are not entirely successful.

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