An Efficient Method for Automatic Recognition of Virus Particles in TEM Images
The conventional approach for virus detection is based on analyzing the negative stain Transmission Electron Microscopy (TEM) images. In this regard, a new method is presented here based on judicious integration of the theory of rough sets and the merits of local texture descriptors. The proposed method identifies the relevant local texture descriptor for representing the intrinsic properties of each pair of virus classes. It also selects important features from each of the relevant descriptors, which can suitably describe significant characteristics of the virus particles present in TEM images. The hypercuboid equivalence partition matrix of rough sets is employed to evaluate the relevance of texture descriptors. Finally, support vector machine (SVM) is used to categorize the virus samples into one of the known virus classes. The efficiency of the proposed method, along with a comparison with related approaches, is demonstrated on publicly available Virus data set.
KeywordsVirus recognition Transmission Electron Microscopy (TEM) images Rough sets Feature selection Local texture descriptor
This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation.
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