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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
An efficient method of automatic pulmonary parenchyma segmentation in CT images
Zhaoxue Chen1, Xiwen Sun, Shengdong Nie
1College of Medical Instrumentation & Foodstuff, University of Shanghai for Science and Technology, Shanghai, 200093, China. chenzhaoxue@163.com
Summary
This study introduces an efficient lung segmentation method for CT images using image thresholding and flood filling. The technique effectively extracts pulmonary parenchyma, demonstrating its validity in experiments.
Area of Science:
- Medical Imaging
- Computer-Aided Diagnosis
- Pulmonary Imaging
Background:
- Accurate lung segmentation is crucial for analyzing computed tomography (CT) scans.
- Existing methods may face challenges with complex image characteristics and computational efficiency.
Purpose of the Study:
- To develop an efficient and simple lung segmentation method for CT images.
- To automatically extract pulmonary parenchyma from lung CT scans.
Main Methods:
- Preprocessing for noise removal.
- Image thresholding combined with fast region flood filling.
- Erosion and area-filtering operations for final lung area extraction.
Main Results:
- The proposed method successfully segments lung CT images.
- Experimental results validate the effectiveness of the lung segmentation technique.
Conclusions:
- The introduced method provides an efficient approach for lung segmentation in CT images.
- This technique simplifies the extraction of pulmonary parenchyma.
