TY - GEN
T1 - A new image data set and benchmark for cervical dysplasia classification evaluation
AU - Xu, Tao
AU - Xin, Cheng
AU - Long, L. Rodney
AU - Antani, Sameer
AU - Xue, Zhiyun
AU - Kim, Edward
AU - Huang, Xiaolei
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths of cervical cancer occur in less developed areas of the world. In this work, we introduce a new image dataset along with ground truth diagnosis for evaluating image-based cervical disease classification algorithms. We collect a large number of cervigram images from a database provided by the US National Cancer Institute. From these images, we extract three types of complementary image features, including Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). PLAB captures color information, PHOG encodes edges and gradient information, and PLBP extracts texture information. Using these features, we run seven classic machine-learning algorithms to differentiate images of high-risk patient visits from those of low-risk patient visits. Extensive experiments are conducted on both balanced and imbalanced subsets of the data to compare the seven classifiers. These results can serve as a baseline for future research in cervical dysplasia classification using images. The image based classifiers also outperform results of several other screening tests on the same datasets.
AB - Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths of cervical cancer occur in less developed areas of the world. In this work, we introduce a new image dataset along with ground truth diagnosis for evaluating image-based cervical disease classification algorithms. We collect a large number of cervigram images from a database provided by the US National Cancer Institute. From these images, we extract three types of complementary image features, including Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). PLAB captures color information, PHOG encodes edges and gradient information, and PLBP extracts texture information. Using these features, we run seven classic machine-learning algorithms to differentiate images of high-risk patient visits from those of low-risk patient visits. Extensive experiments are conducted on both balanced and imbalanced subsets of the data to compare the seven classifiers. These results can serve as a baseline for future research in cervical dysplasia classification using images. The image based classifiers also outperform results of several other screening tests on the same datasets.
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U2 - 10.1007/978-3-319-24888-2_4
DO - 10.1007/978-3-319-24888-2_4
M3 - Conference contribution
AN - SCOPUS:84951948535
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 35
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 5 October 2015
ER -
