Identification and Recognition of Rotavirus- A Particles in Microscopic Images using Enhanced Hybrid Segmentation Methods
Anoop Benny1, Manjunatha Hiremath2
1Dr. Manjunatha Hiremath, Department of Computer Science Department, Christ Deemed to Be University, Bangalore, India.
2Anoop Benny, Department of Computer Science Department, Christ Deemed to Be University, Bangalore, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3316-3323 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8604038620/2020©BEIESP | DOI: 10.35940/ijrte.F8604.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Medical Imaging, Rotavirus-A, Image Segmentation, Classification, Active Counter Model Region Based Extraction, Fuzzy Connectedness, Decision Tree Classifier.
Scope of the Article: Classification.