Novel attribute-based framework for halftone watermarking

A novel attribute-based framework is proposed to tackle the problem of halftone water- marking in combination of the spatial/transformation domain. The challenge is that the host image is continuous, while the watermarked halftone is bi-level. To search for a solution, an attribute image is defined as a good connection between the original grayscale image and its halftone image. When the attribute image is used as a watermark carrier, it helps to fred the watermarked halftone efficiently by solving a constrained modified direct binary search problem. Experimental results demonstrate that the proposed scheme in comparison with other similar methods maintains high watermark capacity with good image quality, high robustness, processing efficiency and easy decoding. Especially it has a good performance in printing application.

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning

Background:Grayscale image attributes of computed tomography(CT)of pulmonary scans contain valuable information relating to patients with respiratory ailments.These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients(49 with COVID-19;8 free of COVID-19)were collected at Namazi Medical Centre(Shiraz,Iran)in 2020 and 2021.Five visual scores(VS:0,1,2,3,or 4)are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions.Eleven deep learning and machine learning techniques(DL/ML)are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49%VS accuracy(18 errors from 513 images)successfully distinguishing VS Classes 0 and 1,outperforming clinicians’visual inspections.An algorithmic score(AS),involving just five grayscale image attributes,is developed independently of clinicians’assessments(99.81%AS accuracy;1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage.The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.