TY - JOUR
T1 - A knowledge-based approach to volumetric medical image segmentation
AU - Chen, Chang Wen
AU - Luo, Jiebo
AU - Parker, K. J.
AU - Huang, T. S.
N1 - Funding Information:
This research was supported by NSF Grant EEC-92-09615 and a New York State Science and Technology Foundation Grant to the Center for Electronic Imaging Systems at the University of Rochester. The authors wish to thank Dr. W. Higgins of Penn State University for providing the CT image data.
Publisher Copyright:
© 1994 IEEE.
PY - 1994
Y1 - 1994
N2 - We propose in this paper an automatic segmentation of 3D image data based on a novel technique using adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final automatic segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of the given object is available.
AB - We propose in this paper an automatic segmentation of 3D image data based on a novel technique using adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final automatic segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of the given object is available.
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U2 - 10.1109/ICIP.1994.413757
DO - 10.1109/ICIP.1994.413757
M3 - Conference article
AN - SCOPUS:84999666541
SN - 1522-4880
VL - 3
SP - 493
EP - 497
JO - Proceedings - International Conference on Image Processing, ICIP
JF - Proceedings - International Conference on Image Processing, ICIP
M1 - 413757
T2 - Proceedings of the 1994 1st IEEE International Conference on Image Processing. Part 3 (of 3)
Y2 - 13 November 1994 through 16 November 1994
ER -