Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

The Norway lobster,Nephrops norvegicus,is one of the main commercial crustacean fisheries in Europe.The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges.The Spanish Oceanographic Institute(IEO)andMarine Institute Ireland(MIIreland)conducts annual underwater television surveys(UWTV)to estimate the total abundance of Nephrops within the specified area,with a coefficient of variation(CV)or relative standard error of less than 20%.Currently,the identification and counting of the Nephrops burrows are carried out manually by the marine experts.This is quite a time-consuming job.As a solution,we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision.The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows.This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification.We conduct experiments on two data sets,namely,the Smalls Nephrops survey(FU 22)and Cadiz Nephrops survey(FU 30),collected by Marine Institute Ireland and Spanish Oceanographic Institute,respectively.From the results,we observe that the Inception model achieved a higher precision and recall rate than theMobileNetmodel.The best mean Average Precision(mAP)recorded by the Inception model is 81.61%compared to MobileNet,which achieves the best mAP of 75.12%.

Deep Trajectory Classification Model for Congestion Detection in Human Crowds

In high-density gatherings,crowd disasters frequently occur despite all the safety measures.Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters.Recent work on the prevention of crowd disasters has been based on manual analysis of video footage.Some methods also measure crowd congestion by estimating crowd density.However,crowd density alone cannot provide reliable information about congestion.This paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian trajectories.The proposed framework divided the input video into several temporal segments.We then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information.A classification model based on convolutional neural networks was then trained using spatio-temporal images.Next,we generated a score map by encoding each point trajectory with its respective class score.After this,we obtained the congested regions by employing the non-maximum suppression method on the score map.Finally,we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.