Abstract
An automatic tissue characterization system is always in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or dependent on heavy human intervention and very time consuming. In this paper, we have developed a highly parallel and effective system based on color image segmentation to analyze liver tissue images. To simplify the tissue classification problem, the system first utilizes the achromatic information (the intensity) to coarsely segment the tissue image, then makes use of the chromatic information to classify the segmented regions into four different tissue classes. Thus, the proposed method includes an unsupervised probabilistic relaxation segmentation process and a supervised Bayes classification process. Because the invariant grey level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be well fulfilled at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Editors | Murray H. Loew |
| Publisher | Publ by Society of Photo-Optical Instrumentation Engineers |
| Pages | 453-464 |
| Number of pages | 12 |
| ISBN (Print) | 0819411310 |
| Publication status | Published - 1993 Dec 1 |
| Event | Medical Imaging 1993: Image Processing - Newport Beach, CA, USA Duration: 1992 Feb 14 → 1992 Feb 19 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 1898 |
| ISSN (Print) | 0277-786X |
Other
| Other | Medical Imaging 1993: Image Processing |
|---|---|
| City | Newport Beach, CA, USA |
| Period | 92-02-14 → 92-02-19 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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Color image analysis for liver tissue images. / Sun, Yung-Nien; Wu, Chung-Hsien; Lin, Xi-Zhang; Chou, Nan Haw.
Proceedings of SPIE - The International Society for Optical Engineering. ed. / Murray H. Loew. Publ by Society of Photo-Optical Instrumentation Engineers, 1993. p. 453-464 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1898).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Color image analysis for liver tissue images
AU - Sun, Yung-Nien
AU - Wu, Chung-Hsien
AU - Lin, Xi-Zhang
AU - Chou, Nan Haw
PY - 1993/12/1
Y1 - 1993/12/1
N2 - An automatic tissue characterization system is always in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or dependent on heavy human intervention and very time consuming. In this paper, we have developed a highly parallel and effective system based on color image segmentation to analyze liver tissue images. To simplify the tissue classification problem, the system first utilizes the achromatic information (the intensity) to coarsely segment the tissue image, then makes use of the chromatic information to classify the segmented regions into four different tissue classes. Thus, the proposed method includes an unsupervised probabilistic relaxation segmentation process and a supervised Bayes classification process. Because the invariant grey level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be well fulfilled at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.
AB - An automatic tissue characterization system is always in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or dependent on heavy human intervention and very time consuming. In this paper, we have developed a highly parallel and effective system based on color image segmentation to analyze liver tissue images. To simplify the tissue classification problem, the system first utilizes the achromatic information (the intensity) to coarsely segment the tissue image, then makes use of the chromatic information to classify the segmented regions into four different tissue classes. Thus, the proposed method includes an unsupervised probabilistic relaxation segmentation process and a supervised Bayes classification process. Because the invariant grey level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be well fulfilled at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.
UR - http://www.scopus.com/inward/record.url?scp=0027884511&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027884511&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027884511
SN - 0819411310
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 453
EP - 464
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Loew, Murray H.
PB - Publ by Society of Photo-Optical Instrumentation Engineers
ER -