Prediction of mouse senescence from HE-Stain liver images using an ensemble SVM classifier

  • Authors:

  • Hui-Ling Huang;Ming-Hsin Hsu;Hua-Chin Lee;Phasit Charoenkwan;Shinn-Jang Ho;Shinn-Ying Ho

  • Affiliations:

  • Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;Department of Automation Engineering, National Huwei Institute of Technology, Yunlin, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan

  • Venue:
  • ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
  • Year:
  • 2013

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Abstract

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.