標題: Prediction of Mouse Senescence from HE-Stain Liver Images Using an Ensemble SVM Classifier
作者: Huang, Hui-Ling
Hsu, Ming-Hsin
Lee, Hua-Chin
Charoenkwan, Phasit
Ho, Shinn-Jang
Ho, Shinn-Ying
生物科技學系
Department of Biological Science and Technology
關鍵字: Aging;cellular senescence;feature selection;genetic algorithm;HE-stain;image analysis;prediction;SVM
公開日期: 2013
摘要: Study of cellular senescence from images in molecular level plays an important role in understanding the molecular basis of ageing. It is desirable to know the morphological variation between young and senescent cells. This study proposes an ensemble support vector machine (SVM) based classifier with a novel set of image features to predict mouse senescence from HE-stain liver images categorized into four classes. For the across-subject prediction that all images of the same mouse are divided into training and test images, the test accuracy is as high as 97.01% by selecting an optimal set of informative image features using an intelligent genetic algorithm. For the leave-one-subject-out prediction that the test mouse is not involved in the training images of 20 mice, we identified eight informative feature sets and established eight SVM classifiers with a single feature set. The best accuracy of using an SVM classifier is 71.73% and the ensemble classifier consisting of these eight SVM classifiers can advance performance with accuracy of 80.95%. The best two feature sets are the gray level correlation matrix for describing texture and Haralick texture set, which are good morphological features in studying cellular senescence.
URI: http://hdl.handle.net/11536/25015
ISBN: 978-3-642-36543-0
ISSN: 0302-9743
期刊: INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT II
Volume: 7803
起始頁: 325
結束頁: 334
顯示於類別:會議論文


文件中的檔案:

  1. 000340592600034.pdf