Identification and Recognition of Rotavirus- A Particles in Microscopic Images using Enhanced Hybrid Segmentation Methods

Abstract

The medical image processing or the medical imaging helps in identifying the different types of diseases that are infected to human body. We know that virus is an infective agent that cannot be seen by the human eyes and this can be viewed with help of a microscope. Rotavirus is type of virus that affect human infants that causes diarrhea, which leads to death in severe condition. Identifying the virus particles in the microscope image is a tedious task. Giving prescription based on the medical diagnosis is dependent how accurately it is done. In this paper, the study of identification, segmentation and feature extraction of the Rotavirus-A in the electron microscopic images is considered. The proposed research method hybridizes four segmentation methods which identifies and classify the rotavirus-A particles in the microscopic image based on their features. The classification is carried out using Decision tree classifier and the accuracy rate is measured. The proposed method yields 96% average accuracy result and this can be improved by considering more dataset and training on it.