Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

Manjula Poojary; Yarramalle Srinivas

doi:10.17485/IJST/v17i20.629

Article

Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i20.629

Year: 2024, Volume: 17, Issue: 20, Pages: 2117-2127

Original Article

Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

Manjula Poojary^1*, Yarramalle Srinivas²

¹Research Scholar, Department of CSE, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India
²Professor, Department of CSE, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:14 April 2024, Accepted Date:06 May 2024, Published Date:18 May 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The research aims to develop the segmentation model to identify the deformity in the medical images as accurately as possible and plan for better medical treatment. The study is extended to identify the disease before its appearance in the human body through human aura images to support aura imaging in medical diagnosis. Methods: The study used a brain image from the UCI data set and Aura images from the Biowell data set to identify the disease. The segmentation model Bivariate Gaussian Mixture Model (B.G.M.M) was developed. Model parameters are derived using the Expectation Maximization (E.M) Algorithm. The Grasshopper optimization Algorithm (G.O.A) extracts optimal features from the images. The chosen feature is fed as input to the classification model B.G.M.M. Segmentation accuracy is measured using the quality metrics. Findings: The developed approach shows 97% accuracy in identifying the damaged tissues in MRI images and high-intensity energy zones in the aura images, indicating the potential for deformities. Novelty: This study significantly contributes to the field by offering novel solutions for precise and comprehensive image analysis in medical and aura imaging contexts.

Keywords: G.O.A, segmentation, G.M.M, E.M, quality metrics, deformity identification, Hue and saturation

References

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Copyright

© 2024 Poojary & Srinivas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)