@inproceedings{b76deaad04f744ae8e2da46b71fb7919,
title = "The Classification of Meningioma Subtypes Based on the Color Segmentation and Shape Features",
abstract = "This paper proposed an automatic method for the classification of meningioma subtypes based on the unsupervised color segmentation method and feature selection scheme. Firstly, a color segmentation method is utilized to segment the cell nuclei. Then the set of shape feature vectors which are calculated from the segmentation results are constructed. Finally, a k-nearest neighbour classifier (kNN) is used to classify the meningioma subtypes. Experiment shows that the classification accuracy of 85 \% is achieved by using a leave-one-out cross validation approach on 80 meningioma images. ",
keywords = "Classification, Color, Meningioma, Segmentation, Shape features",
author = "Ziming Zeng and Zeng Tong and Zhonghua Han and Yinlong Zhang and Reyer Zwiggelaar",
year = "2013",
month = dec,
day = "6",
doi = "10.1007/978-94-007-7618-0\_335",
language = "English",
isbn = "9789400776173",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Nature",
pages = "2669--2674",
editor = "Shaozi Li and Qun Jin and Xiaohong Jiang and Park, \{James J. (Jong Hyuk)\}",
booktitle = "Frontier and Future Development of Information Technology in Medicine and Education, ITME 2013",
address = "Switzerland",
}