Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework
subject: Chan–Vese level set method; Image segmentation; Minimum distance classifier; Particle filtering; Sequential importance sampling
description: Image processing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. So far, many algorithms and methods are proposed in the state-of-the-art literature. But still, the exact identification of region of interest in biomedical image is a research topic. In this paper, E. coli bacteria particle segmentation and classification is proposed. For the current research work, the hybrid algorithm is developed based on sequential importance sampling (SIS) framework, particle filtering, and Chan–Vese level set method. The proposed research work produces 95.50% of average classification accuracy. 2019, Springer Nature Singapore Pte Ltd.
creator: Hiremath M.
source: Advances in Intelligent Systems and Computing, Vol-882, pp. 503-512.
publisher: Springer Verlag
date: 2019-01-01
rights: Restricted Access
relation: ISSN: 21945357; ISBN: 978-981135952-1
format: Online
language: English
type: Conference paper
coverage: Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India