1. Automated Pleural Line Detection Based on Radon Transform Using Ultrasound
- Author
-
Qingli Li, Chao He, Wenfang Li, Jiangang Chen, and Jiawei Li
- Subjects
medicine.medical_specialty ,Computer science ,Image processing ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Ultrasonography ,Radiological and Ultrasound Technology ,Radon transform ,business.industry ,Ultrasound ,030208 emergency & critical care medicine ,respiratory system ,medicine.disease ,respiratory tract diseases ,Data set ,Pneumothorax ,Radon ,Ultrasonic sensor ,Radiology ,Line (text file) ,business ,Pleural line - Abstract
It is of vital importance to identify the pleural line when performing lung ultrasound, as the pleural line not only indicates the interface between the chest wall and lung, but offers additional diagnostic information. In the current clinical practice, the pleural line is visually detected and evaluated by clinicians, which requires experiences and skills with challenges for the novice. In this study, we developed a computer-aided technique for automated pleural line detection using ultrasound. The method first utilized the Radon transform to detect line objects in the ultrasound images. The relation of the body mass index and chest wall thickness was then applied to estimate the range of the pleural thickness, based on which the pleural line was detected together with the consideration of the ultrasonic properties of the pleural line. The proposed method was validated by testing 83 ultrasound data sets collected from 21 pneumothorax patients. The pleural lines were successfully identified in 76 data sets by the automated method (successful detection rate 91.6%). In those successful cases, the depths of the pleural lines measured by the automated method agreed with those manually measured as confirmed with the Bland-Altman test. The measurement errors were below 5% in terms of the pleural line depth. As a conclusion, the proposed method could detect the pleural line in an automated manner in the defined data set. In addition, the method may potentially act as an alternative to visual inspection after further tests on more diverse data sets are performed in future studies.
- Published
- 2020