HEp-2 Cell Image Classification: A Comparative Analysis

  • Praful Agrawal
  • Mayank Vatsa
  • Richa Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


HEp-2 cell image classification is an important and relatively unexplored area of research. This paper presents an experimental analysis of five different categories of feature sets with four different classifiers to determine the best performing combination of features and classifiers. The analysis is performed on the ICIP 2013 Cell Image Classification Contest Training dataset comprising over 13,000 cell images pertaining to six cell classes. The results computed with 10 fold cross validation show that texture features perform the best among all the explored feature sets and the combination of Laws features with SVM yields the highest accuracy.


Cell Image Fold Cross Validation Feature Category Cell Pattern Cell Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Praful Agrawal
    • 1
  • Mayank Vatsa
    • 1
  • Richa Singh
    • 1
  1. 1.Indraprastha Institute of Information TechnologyDelhiIndia

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