Pleural Line Detection Enhancement in Lung Ultrasonography (LUS) Based on Morphological and Adaptive Structural 2D Filter

Abstract

Lung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healt

ung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healthy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer.

hy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer.