Normalized Cross Correlation Research Articles

A Two-Level Security Layer for Medical Data

Implementing a two-level security approach comprising cryptography and steganography in Electronic Health Record (EHR) systems ensures comprehensive protection of sensitive patient information. Cryptography encryption methods alone may not provide adequate protection, as they can be susceptible to attacks and also, steganography, has the visual quality issues after embedding. The combination of RSA cryptography and DWT steganography will offers a comprehensive solution for securing medical data, ensuring confidentiality, integrity, and compliance with privacy regulations, while DWT steganography provides covert embedding within medical images, preserving diagnostic integrity and enhancing data protection. In this paper, a secure two-level layer of medical data security is proposed to encrypt secret information using Rivest-Shamir-Adleman (RSA) while the encrypted secret information is embedded using Discrete Wavelet Transform (DWT). The performance of the proposed method was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) metrics. The results obtained show that our method performs with a value of 0.105 for MSE, 49.713 for PSNR and 0.912 for NCC showing high visual quality and low imperceptibility.

Ultrasonic phased array imaging of multi-layered structures using full-matrix migration and normalized cross-correlation matching technique

Multi-layered structures play a critical role in aerospace, automotive and electrical industries, so the non-destructive evaluation and maintenance of such structures must be accurate and rapid. The application of conventional ultrasonic phased array imaging methods is limited due to computational complexity, image quality and image size. In this paper, we proposed a cost-effective, high-quality imaging method that uses full-matrix migration total focus method (FM-TFM) and produces large-area scanned image with the aid of normalized cross-correlation (NCC) template matching technique. The FM-TFM method eliminated the need for iterative refraction point calculations by using full matrix migration extrapolation, and the optimization of NCC was also investigated in the study. The verification was divided into two parts, fixed transducer experiment and manual scanned experiment, to demonstrate the advantages of FM-TFM with improved SNR (22 % or greater) and one order of magnitude reduction in computational time when compared to ray tracing total focus method (RT-TFM) and NCC matching accuracy of multilayer specimen, respectively. The proposed method can help eliminate the need for costly scanning equipment and shows great potential for extension to other TFM-based approaches.

Robust digital image watermarking scheme with a fractional-order discrete-time chaotic scheme and DWT-SVD transform

A robust digital image watermarking system with a fractional-order discrete-time chaotic system and discrete wavelet transform-singular value decomposition is presented. The inclusion method inserts an encrypted image into the dynamics of an integer-order discrete-time chaotic system and the resulting cipher serves as a host image watermark. A watermarking function (DWT-SVD transform) watermarks a host image before the watermark is extracted and decrypted at the receiver. As a contribution, our suggested approach introduces a new watermarking system based on discrete-time chaotic systems, as well as a hybridization of integer and fractional-order systems to ensure the watermarking scheme’s robustness. Our method achieved good results in terms of robustness, with normalized cross correlation values above 0.99 when subjected to a range of attacks. Results of the simulation underline practicality and robustness achieved by this approach. They also indicate that the proposed system resists various attacks.

A comparative analysis of pairwise image stitching techniques for microscopy images

Stitching of microscopic images is a technique used to combine multiple overlapping images (tiles) from biological samples with a limited field of view and high resolution to create a whole slide image. Image stitching involves two main steps: pairwise registration and global alignment. Most of the computational load and the accuracy of the stitching algorithm depend on the pairwise registration method. Therefore, choosing an efficient, accurate, robust, and fast pairwise registration method is crucial in the whole slide imaging technique. This paper presents a detailed comparative analysis of different pairwise registration techniques in terms of execution time and quality. These techniques included feature-based methods such as Harris, Shi-Thomasi, FAST, ORB, BRISK, SURF, SIFT, KAZE, MSER, and deep learning-based SuperPoint features. Additionally, region-based methods were analyzed, which were based on the normalized cross-correlation (NCC) and the combination of phase correlation and NCC. Investigations have been conducted on microscopy images from different modalities such as bright-field, phase-contrast, and fluorescence. The feature-based methods were highly robust to uneven illumination in tiles. Moreover, some features were found to be more accurate and faster than region-based methods, with the SURF features identified as the most effective technique. This study provides valuable insights into the selection of the most efficient and accurate pairwise registration method for creating whole slide images, which is essential for the advancement of computational pathology and biology.

Lamb waves-based PCF-DMA: An anti-interference synchronous independent data transmission scheme for multiple cross-space users

Due to the high cost and safety risk brought about by wire penetration within the aerospace and underwater structure, reliable wireless cross-space multiple access transmission techniques become highly necessary. Lamb waves data transmission is recognized as a feasible solution, since it utilizes the solid structures as the transmission medium without being affected by electromagnetic radiation. However, in the case of synchronous transmission of multiple users, the aggravation of inter-access interference would severely weaken the reliability of the transmission system. Focusing on the issue of anti-interference synchronous independent data transmission for multiple cross-space users, this study proposes a Lamb waves-based pulse code frequency division multiple access (PCF-DMA) scheme. Firstly, a fast pulse position modulation strategy is established to improve the transmission rate. Considering the characteristics of the Lamb waves excitation pulses as the codeword, a PCF-DMA multiple access encoding method is designed. Secondly, by sparsely representing the Lamb waves physical transmission channel, a shift-invariant sparse deconvolution technology is proposed for decoding. Specially, to achieve reliable multiple access transmission with the PCF-DMA scheme, a normalized cross-correlation coefficient is used as the design criterion of the Lamb wave pulses. To validate these concepts, a three-user PCF-DMA data transmission system based on Lamb waves is built. The experimental results illustrate that the proposed design criterion can accurately predict the reliability of the multiple access transmission system. The bit error rate (BER) of the proposed PCF-DMA scheme is 30 % lower than that of conventional FDMA within a signal-to-noise ratio of −12 to 8 dB. Further, the influence factor analysis for the BER and communication rate are conducted. Finally, a synchronous independent data transmission for multiple cross-space users is achieved, with a maximum communication rate of 92.9 kbps per user and a BER below 0.17 %.

Normalized weighted cross correlation for multi-channel image registration

The normalized cross-correlation (NCC) is widely used for image registration due to its simple geometrical interpretation and being feature-agnostic. Here, after reviewing NCC definitions for images with an arbitrary number of dimensions and channels, we propose a generalization in which each pixel value of each channel can be individually weighted using real non-negative numbers. This generalized normalized weighted cross-correlation (NWCC) and its zero-mean equivalent (ZNWCC) can be used, for example, to prioritize pixels based on signal-to-noise ratio. Like a previously defined NWCC with binary weights, the proposed generalizations enable the registration of uniformly, but not necessarily isotropically, sampled images with irregular boundaries and/or sparse sampling. All NCC definitions discussed here are provided with discrete Fourier transform (DFT) formulations for fast computation. Practical aspects of NCC computational implementation are briefly discussed, and a convenient function to calculate the overlap of uniformly, but not necessarily isotropically, sampled images with irregular boundaries and/or sparse sampling is introduced, together with its DFT formulation. Finally, examples illustrate the benefit of the proposed normalized cross-correlation functions.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278±0.122%, PSNR of 33.783±0.824dB, SSIM of 0.964±0.009, NCC of 0.968±0.011, HRS of 4.543, and SUV Error of 0.255±0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973±0.066%, PSNR of 36.172±0.801dB, SSIM of 0.984±0.004, NCC of 0.990±0.005, HRS of 4.428, and SUV Error of 0.151±0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the multi-scale normalized cross correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Image compression and reconstruction using improved Stockwell transform for quality enhancement

Image compression is an important stage in picture processing since it reduces the data extent and promptness of image diffusion and storage, whereas image reconstruction helps to recover the original information that was communicated. Wavelets are commonly cited as a novel technique for image compression, although the production of waves proceeding smooth areas with the image remains unsatisfactory. Stockwell transformations have been recently entered the arena for image compression and reconstruction operations. As a result, a new technique for image compression based on the improved Stockwell transform is proposed. The discrete cosine transforms, which involves bandwidth partitioning is also investigated in this work to verify its experimental results. Wavelet-based techniques such as multilevel Haar wavelet, generic multiwavelet transform, Shearlet transform, and Stockwell transforms were examined in this paper. The MATLAB technical computing language is utilized in this work to implement the existing approaches as well as the suggested improved Stockwell transform. The standard images mostly used in digital image processing applications, such as Lena, Cameraman and Barbara are investigated in this work. To evaluate the approaches, quality constraints such as mean square error (MSE), normalized cross-correlation (NCC), structural content (SC), peak noise ratio, average difference (AD), normalized absolute error (NAE) and maximum difference are computed and provided in tabular and graphical representations.

Towards ASSR-based hearing assessment using natural sounds

Objective. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG). Ear-EEG can potentially be integrated into hearing aids, which would enable automatic fitting of the hearing device in daily life. The conventional stimuli for ASSR-based hearing assessment, such as pure tones and chirps, are monotonous and tiresome, making them inconvenient for repeated use in everyday situations. In this study we investigate the use of natural speech sounds for ASSR estimation. Approach. EEG was recorded from 22 normal hearing subjects from both scalp and ear electrodes. Subjects were stimulated monaurally with 180 min of speech stimulus modified by applying a 40 Hz amplitude modulation (AM) to an octave frequency sub-band centered at 1 kHz. Each 50 ms sub-interval in the AM sub-band was scaled to match one of 10 pre-defined levels (0–45 dB sensation level, 5 dB steps). The apparent latency for the ASSR was estimated as the maximum average cross-correlation between the envelope of the AM sub-band and the recorded EEG and was used to align the EEG signal with the audio signal. The EEG was then split up into sub-epochs of 50 ms length and sorted according to the stimulation level. ASSR was estimated for each level for both scalp- and ear-EEG. Main results. Significant ASSRs with increasing amplitude as a function of presentation level were recorded from both scalp and ear electrode configurations. Significance. Utilizing natural sounds in ASSR estimation offers the potential for electrophysiological hearing assessment that are more comfortable and less fatiguing compared to existing ASSR methods. Combined with ear-EEG, this approach may allow convenient hearing threshold estimation in everyday life, utilizing ambient sounds. Additionally, it may facilitate both initial fitting and subsequent adjustments of hearing aids outside of clinical settings.

Spatio-temporal void fraction visualization in air-water two-phase flow regime transitions by combination of convolutional neural network and long short-term memory implemented into multiple current-voltage (MCV-CNN_LSTM)

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.

Binocular Visual Measurement Method Based on Feature Matching.

To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.

Removal of salt and pepper noise using adaptive switching modified decision-based unsymmetric trimmed median filter optimized with Hyb-BCO-FBIA

An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted -norm to regularize the deformation field instead of the traditional -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical -test results also proved that these improvements of our method have statisticalsignificance. In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

Radargrammetric 3D Imaging through Composite Registration Method Using Multi-Aspect Synthetic Aperture Radar Imagery

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data.

A star under multiple influences. Magnetic activity in V815 Her, a compact 2+2 hierarchical system

Close binaries with magnetically active components are astrophysical laboratories for studying the effects of binarity on activity. Of particular interest are binary and multiple star systems that contain a solar-type active component with an internal structure similar to the Sun, allowing us to study how the dynamo of a solar-type star would work under different conditions. We have conducted a comprehensive investigation of V815\,Her using photometric and spectroscopic data to understand the origin of the activity and what influences it in the short and long term. Using space photometry we performed light curve modeling in order to derive astrophysical and orbital parameters for the eclipsing binary subsystem V815\,Her B. Using archival photometric data covering a century we carried out a time frequency analysis. Spectral synthesis was applied to determine the basic astrophysical parameters of the rapidly rotating primary using high-resolution STELLA spectra recorded in 2018. Photometric analysis of archived data revealed multiple cycles on timescales between sim 6.5 and sim 26 years, some of which may be harmonic. From TESS photometry we obtained an orbital solution for the V815\,Her B subsystem. By placing the primary component on the Hertzsprung$-$Russell-diagram, we could deduce an age of approx 30\,Myr, in line with the high Li-6707 abundance. The STELLA spectra covering the 200 day-long observing season enabled us to create 19 time-series Doppler images, which revealed a constantly changing spotted surface on a timescale of a few weeks. From the consecutive image pairs we built up the average cross-correlation function map to measure the surface differential rotation of the spotted star, from which we derive a weak solar-type surface shear. We found evidence that the V815\,Her B component previously apostrophized as a "third body" is actually an eclipsing close binary subsystem of two M dwarfs with a period of 0.5\,d, that is, V815\,Her is a 2+2 hierarchical quadruple system. The system is apparently young, only a few times ten million years old, consistent with the spotted primary V815\,Her Aa being a zero-age main-sequence star. Spot activity on the primary was found to be vivid. Fast starspot decay suggests that convective-turbulent erosion plays a more significant role in such a rapidly rotating star. The weak surface shear of V815\,Her Aa due to differential rotation is presumably confined by tidal forces of the close companion V815\,Her Ab. The slowly increasing photometric cycle of about 6.5 years on average is interpreted as a spot cycle of V815\,Her Aa, which is probably modulated by the eccentric wide orbit.

Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.

Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.

Color Image Watermarking Using Adaptive Tunable Q Wavelet, Finite Ridge Let, and Arnold Transforms with TSVD

In recent years, researchers have been working to integrate watermarking practices anchored in wavelet transform (WT) coefficients and Singular value decomposition (SVD) to recover the robustness of watermarked images in opposition to geometric and non-geometric attacks. Conversely, the watermark embedding, can initiate vagueness in the false-positive problem (FPP). To address this issue, Flamingo search algorithm-based Color Image Watermarking using Adaptive Tunable Q wavelet, Finite ridge let, Arnold transforms with Truncated Singular value decomposition (ATQWT-FRT-AT-TSVD-FSOA-CIW) is proposed. The proposed ATQWT-FRTAT-TSVD-FSOA-CIW method is separated into two stages: (i) watermark embedding and (ii) watermark extraction. The scaling factor is optimized with the help of the Flamingo search algorithm, which provides higher robustness, higher embedding strength, resistance to a false-positive problem, and better quality of the watermarking scheme. The proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method is simulated in MATLAB tool. The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW design is estimated under several performance metrics, such as Peak signal to noise ratio (PSNR), Structural similarity index measurement (SSIM), and Normalized cross-correlation (NCC). The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method attains 35%, 22.43%, 21.157%, 13.96%, and 7.186% higher PSNR compared to the existing designs, like secure with robust color image watermarking utilizing nature-inspired intelligence (DWT-SVD-ABCOA-CIW), the Artificial bee colony basis perceptually tuned blind color image watermarking in the hybrid LWT-DCT domain (LWT-DCT-ABCOA-CIW), the DWT-SVD based adaptive color multi-watermarking scheme for copyright protection under AMEF and PSO-GWO (DWT-SVD-Hybrid PSO-GWOA-CIW), Robust image watermarking based on fractional Krawtchouk transform with optimization (FrKT-Hybrid FO-CSOA-CIW), and the optimized semi-blind scheme for color image watermarking (DWTFRT-AT-SVD-PSO-CIW).

Automated three-dimensional image registration for longitudinal photoacoustic imaging.

Photoacoustic tomography (PAT) has great potential in monitoring disease progression and treatment response in breast cancer. However, due to variations in breast repositioning, there is a chance of geometric misalignment between images. Further, poor repositioning can affect light fluence distribution and imaging field-of-view, making images different from one another. The net effect is that it becomes challenging to distinguish between image changes due to repositioning effects and those due to true biological variations. The aim is to develop a three-dimensional image registration framework for geometrically aligning repeated PAT volumetric images, which are potentially affected by repositioning effects such as misalignment, changed radiant exposure conditions, and different fields-of-view. The proposed framework involves the use of a coordinate-based neural network to represent the displacement field between pairs of PAT volumetric images. A loss function based on normalized cross correlation and Frangi vesselness feature extraction at multiple scales was implemented. We refer to our image registration framework as MUVINN-reg, which stands for multiscale vesselness-based image registration using neural networks. The approach was tested on a longitudinal dataset of healthy volunteer breast PAT images acquired with the hybrid photoacoustic-ultrasound Photoacoustic Mammoscope 3 imaging system. The registration performance was also tested under unfavorable repositioning conditions such as intentional mispositioning, and variation in breast-supporting cup size between measurements. A total of 13 pairs of repeated PAT scans were included in this study. MUVINN-reg showed excellent performance in co-registering each pair of images. The proposed framework was shown to be robust to image intensity shifts and field-of-view changes. Furthermore, MUVINN-reg could align vessels at imaging depths greater than 4cm. The proposed framework will enable the use of PAT for quantitative and reproducible monitoring of disease progression and treatment response.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETd distributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETd distribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETd map generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETd maps generated by the proposed method are 0.096 ± 0.019 keV μm−1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keV μm−1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETd maps from dose maps and has the potential to improve proton therapy planning by providing accurate LETd information.