DocumentCode :
2423565
Title :
A boundary optimisation algorithm for delineating brain objects from CT-scans
Author :
Li, Hongyi ; Nyssen, Edgard ; Cornelis, Jens
Author_Institution :
Dept. of Electron. Eng., Vrije Univ., Brussels, Belgium
fYear :
1993
fDate :
31 Oct-6 Nov 1993
Firstpage :
1553
Abstract :
The authors present an image processing algorithm for contour refinement of anatomical objects from brain CT scans. The algorithm uses an optimisation function to detect the contour points and to determine the belief that a point is lying on the object boundary. This optimisation function takes into account the following image features: the amplitude of the gradient, the orientation change of the gradient along the contour and a curvature measure. The proposed technique consists of using the three image features as variables in a Fisher linear classifier. That means a linear transformation vector WT =(α1, α2, α3) must be determined to achieve a maximal separation of two sets of points-namely boundary points and non-boundary points. The training sets for the Fisher linear classifier are acquired by a manual selection process, yielding typical boundary points and non-boundary points for different brain objects. The authors also discuss experiments in which the technique is applied to CT-scans of normal human brains
Keywords :
brain; computerised tomography; edge detection; medical image processing; optimisation; Fisher linear classifier variables; anatomical objects; boundary optimisation algorithm; brain objects delineation; contour points detection; contour refinement; image features; image processing algorithm; medical diagnostic imaging; nonboundary points; normal human brains; Change detection algorithms; Computed tomography; Humans; Image edge detection; Image processing; Image segmentation; Iris; Labeling; Object detection; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record.
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-1487-5
Type :
conf
DOI :
10.1109/NSSMIC.1993.373551
Filename :
373551
Link To Document :
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