Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

Abstract

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.

Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19

“…The ResNet18, ResNet50, SqueezeNet, and DenseNet-121 models on the CNN method can identify sufferers of COVID-19 [20]. [23].…”

A deep transfer learning based model for automatic detection of COVID-19 from chest X-ray

“…Finally, they applied a depth-wise separable CNN (DSCNN) to make their COVIDLite model. Siracusano et al [10] used a fast and adaptive bidimensional decomposition and CLAHE for enhancing the necessary features from the chest x-rays. They used it in the medical domain for image retrieval and fusion.…”

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

“…Images available for training may not be of uniform contrast or brightness for the neural network to identify features within them [16]. To improve the contrast improvement index, entropy and measure of enhancement [17], Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for their normalization. The images are split into regions and histograms are calculated for each region which are then interpolated in between.…”