TY - GEN
T1 - Curvelet-based texture classification of critical Gleason patterns of prostate histological images
AU - Lin, Wen Chyi
AU - Li, Ching Chung
AU - Epstein, Jonathan I.
AU - Veltri, Robert W.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/30
Y1 - 2016/12/30
N2 - This paper presents our new result of a study on machine-aided classification of four critical Gleason patterns with curvelet-based texture descriptors extracted from prostatic histological section images. The reliable recognition of these patterns between Gleason score 6 and Gleason score 8 is of crucial importance that will affect the appropriate treatment and patient's quality of life. Higher-order statistical moments of fine scale curvelet coefficients are selected as discriminative features. A two-level classifier consisting of two Gaussian kernel support vector machines, each incorporated with a pertinent voting mechanism by multiple windowed patches in an image for final decision making, has been developed. A set of Tissue MicroArray (TMA) images of four prominent Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3 and 4 + 4 has been studied in machine learning and testing. The testing result has achieved an average accuracy of 93.75% for 4 classes, an outstanding performance when compared with other published works.
AB - This paper presents our new result of a study on machine-aided classification of four critical Gleason patterns with curvelet-based texture descriptors extracted from prostatic histological section images. The reliable recognition of these patterns between Gleason score 6 and Gleason score 8 is of crucial importance that will affect the appropriate treatment and patient's quality of life. Higher-order statistical moments of fine scale curvelet coefficients are selected as discriminative features. A two-level classifier consisting of two Gaussian kernel support vector machines, each incorporated with a pertinent voting mechanism by multiple windowed patches in an image for final decision making, has been developed. A set of Tissue MicroArray (TMA) images of four prominent Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3 and 4 + 4 has been studied in machine learning and testing. The testing result has achieved an average accuracy of 93.75% for 4 classes, an outstanding performance when compared with other published works.
KW - Gleason grading
KW - critical Gleason scores
KW - curvelets
KW - histopathology image analysis
KW - prostate cancer
UR - http://www.scopus.com/inward/record.url?scp=85011072586&partnerID=8YFLogxK
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U2 - 10.1109/ICCABS.2016.7802768
DO - 10.1109/ICCABS.2016.7802768
M3 - Conference contribution
AN - SCOPUS:85011072586
T3 - 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2016
BT - 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2016
Y2 - 13 October 2016 through 15 October 2016
ER -