Phase congruency-based filtering approach combined with a convolutional network for lung CT image analysis

ABSTRACT

Computed tomography (CT) imaging was confirmed as one of the important evolving radiological features of disease diagnosis. In this work, we propose an image filtering method for CT images that fuses the spatial and the transform domain. Filters in the transform domain work well in restoring low-contrast details that are usually smoothed by spatial domain filters. The new filter is based on a modified anisotropic diffusion approach combined with the phase congruency (PC) feature. The PC feature is a scale-invariant compared to the classical gradient, where weak edges are usually omitted and undetected with gradient-based feature detectors. This feature is incorporated in the diffusion function to enhance image edges while eliminating noise and texture background. In a further step, a U-net convolutional network is used to segment the lung-infected area. Comparative tests and results show the efficiency of our method, which permits to enhance image features while removing noise perfectly.

KEYWORDS:

• COVID-19
• CT imaging
• anisotropic diffusion
• phase congruency
• noise reduction
• image enhancement
• edge sharpening
• U-net convolutional network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohamed Ben Gharsallah

Mohamed Ben Gharsallah received the master degree in automatic control and the Phd thesis degree in electrical engineering from the National Higher School of Engineering (ENSIT), University of Tunis. He is a member at the research laboratory in intelligent robotics, reliability, image and signal processing (RIFTSI).His research interests include computer vision, image processing, pattern recogntion and machine learning.

Hassene Seddik

Hassene Seddik obtained an electromechanical engineer degree and followed by the Master’s degree in signal processing: “speaker recognition” and thesis degree in image processing “watermarking using non conventional transformations”. Currently, he is a full professor at the national higher school of engineering (ENSIT), university of Tunis. His domain of interest is: Audio-image and video processing applied in filtering, encryption and watermarking, deep learning and applications.