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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
Pages325-334
Number of pages10
EditionPART 2
DOIs
StatePublished - 11 Mar 2013
Event5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013 - Kuala Lumpur, Malaysia
Duration: 18 Mar 201320 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7803 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013
CountryMalaysia
CityKuala Lumpur
Period18/03/1320/03/13

Keywords

  • Aging
  • HE-stain
  • SVM
  • cellular senescence
  • feature selection
  • genetic algorithm
  • image analysis
  • prediction

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