TY - GEN
T1 - A quantitative analysis system of pulmonary nodules CT image for lung cancer risk classification
AU - Le, Vanbang
AU - Zhu, Yu
AU - Yang, Dawei
AU - Zheng, Bingbing
AU - Ren, Xiaodong
N1 - Publisher Copyright:
© 2018, Springer Nature Singapore Pte Ltd.
PY - 2018
Y1 - 2018
N2 - To improve the classification performance for lung nodule, we proposed a lung nodule CT image feature extraction method. The approach designed multi-directional distribution features to represent nodules in different risk stages effectively. First, the reference map is constructed using integral image, and then K-Means approach is performed to clustering the reference map and calculate its label map. The density distribution map of lung nodule image was generated after calculate the gray scale density distribution level for each pixel. An exponential function was designed to weighting the angular histogram for each components of the distribution map. Then, quantitative measurement was performed by Random Forest classifier. The evaluation dataset is the lung CT database which provided by Shanghai Zhongshan Hospital (ZSDB), the nodule risk categories were AAH, AIS, MIA, and IA. In the result the AUCs are 0.9771, 0.9917, 0.9590, 0.9971, and accuracy are 0.7478, 0.9167, 0.7450, 0.9567 for AAH, AIS, MIA and IA respectively. The experiments show that the proposed method performs well and is effective to improve the classification performance of pulmonary nodules.
AB - To improve the classification performance for lung nodule, we proposed a lung nodule CT image feature extraction method. The approach designed multi-directional distribution features to represent nodules in different risk stages effectively. First, the reference map is constructed using integral image, and then K-Means approach is performed to clustering the reference map and calculate its label map. The density distribution map of lung nodule image was generated after calculate the gray scale density distribution level for each pixel. An exponential function was designed to weighting the angular histogram for each components of the distribution map. Then, quantitative measurement was performed by Random Forest classifier. The evaluation dataset is the lung CT database which provided by Shanghai Zhongshan Hospital (ZSDB), the nodule risk categories were AAH, AIS, MIA, and IA. In the result the AUCs are 0.9771, 0.9917, 0.9590, 0.9971, and accuracy are 0.7478, 0.9167, 0.7450, 0.9567 for AAH, AIS, MIA and IA respectively. The experiments show that the proposed method performs well and is effective to improve the classification performance of pulmonary nodules.
KW - Angular histogram
KW - Exponential weighting
KW - Gray scale density distribution
KW - Lung CT image
KW - Nodule classification
UR - http://www.scopus.com/inward/record.url?scp=85042108132&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-8108-8_2
DO - 10.1007/978-981-10-8108-8_2
M3 - Conference contribution
AN - SCOPUS:85042108132
SN - 9789811081071
T3 - Communications in Computer and Information Science
SP - 12
EP - 24
BT - Digital TV and Wireless Multimedia Communication - 14th International Forum, IFTC 2017, Revised Selected Papers
A2 - Yang, Xiaokang
A2 - Zhai, Guangtao
A2 - Zhou, Jun
PB - Springer Verlag
T2 - 14th International Forum of Digital TV and Wireless Multimedia Communication, IFTC 2017
Y2 - 8 November 2017 through 9 November 2017
ER -