Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

TL;DR

This paper presents an automated system combining Harmony-Search optimization and Otsu thresholding to detect and evaluate COVID-19 pneumonia in lung CT scans, aiding diagnosis and treatment planning.

Contribution

It introduces a novel image processing pipeline that enhances lung CT analysis for COVID-19 detection using Harmony-Search and Otsu methods, improving accuracy and efficiency.

Findings

Effective lung region extraction with threshold filtering

Accurate infection segmentation using combined optimization and thresholding

Quantitative assessment of infection severity through ROI features

Abstract

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding; (iii) Image segmentation to extract infected region(s); and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden.