Classification of Tuberculosis Digital Images Using Hybrid Evolutionary Extreme Learning Machines

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7653)


In this work, classification of Tuberculosis (TB) digital images has been attempted using active contour method and Differential Evolution based Extreme Learning Machines (DE-ELM). The sputum smear positive and negative images (N=100) recorded under standard image acquisition protocol are subjected to segmentation using level set formulation of active contour method. Moment features are extracted from the segmented images using Hu’s and Zernike method. Further, the most significant moment features derived using Principal Component Analysis and Kernel Principal Component Analysis (KPCA) are subjected to classification using DE-ELM. Results show that the segmentation method identifies the bacilli retaining their shape in-spite of artifacts present in the images. It is also observed that with the KPCA derived significant features, DE-ELM performed with higher accuracy and faster learning speed in classifying the images.


Tuberculosis Sputum smear images Active contours Level sets Principal Component Analysis Kernel Principal Component Analysis Hu’s moments Zernike moments Extreme learning machines Differential evolution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Instrumentation Engineering, MIT CampusAnna UniversityChennaiIndia
  2. 2.Biomedical Engineering Group, Department of Applied MechanicsIIT MadrasChennaiIndia

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