Abstract
In this Paper, we introduce a new method for automatic recognition and classification of prostate cancer biopsy images based on Gleason grading system. The introduced algorithm combines features from wavelet transform and fractal analysis domains. Biopsy images are pre-processed prior to features extraction using effective image processing algorithms to analyze textural complexity in terms of RGB color channels, edge and segmentation information. Experimental results achieved an average classification accuracy of 95 % in a set of 45 images with diversities in resolution, magnification levels, and stain colors.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Pages | 2696-2701 |
| Number of pages | 6 |
| DOIs | |
| State | Published - 2011 |
| Event | 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States Duration: Oct 9 2011 → Oct 12 2011 |
Other
| Other | 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 |
|---|---|
| Country | United States |
| City | Anchorage, AK |
| Period | 10/9/11 → 10/12/11 |
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Keywords
- fractal dimension
- Gleason grading
- Prostate cancer
- statistical classification
- wavelet features
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction
Cite this
Gleason grade-based automatic classification of prostate cancer pathological images. / Almuntashri, Ali; Agaian, Sos; Thompson, Ian; Rabah, Danny; Zin Al-Abdin, Osman; Nicolas, Marlo.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2011. p. 2696-2701 6084080.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Gleason grade-based automatic classification of prostate cancer pathological images
AU - Almuntashri, Ali
AU - Agaian, Sos
AU - Thompson, Ian
AU - Rabah, Danny
AU - Zin Al-Abdin, Osman
AU - Nicolas, Marlo
PY - 2011
Y1 - 2011
N2 - In this Paper, we introduce a new method for automatic recognition and classification of prostate cancer biopsy images based on Gleason grading system. The introduced algorithm combines features from wavelet transform and fractal analysis domains. Biopsy images are pre-processed prior to features extraction using effective image processing algorithms to analyze textural complexity in terms of RGB color channels, edge and segmentation information. Experimental results achieved an average classification accuracy of 95 % in a set of 45 images with diversities in resolution, magnification levels, and stain colors.
AB - In this Paper, we introduce a new method for automatic recognition and classification of prostate cancer biopsy images based on Gleason grading system. The introduced algorithm combines features from wavelet transform and fractal analysis domains. Biopsy images are pre-processed prior to features extraction using effective image processing algorithms to analyze textural complexity in terms of RGB color channels, edge and segmentation information. Experimental results achieved an average classification accuracy of 95 % in a set of 45 images with diversities in resolution, magnification levels, and stain colors.
KW - fractal dimension
KW - Gleason grading
KW - Prostate cancer
KW - statistical classification
KW - wavelet features
UR - http://www.scopus.com/inward/record.url?scp=83755229158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83755229158&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2011.6084080
DO - 10.1109/ICSMC.2011.6084080
M3 - Conference contribution
AN - SCOPUS:83755229158
SN - 9781457706523
SP - 2696
EP - 2701
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ER -