Structural Similarity Index Matrix Research Articles

Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption.

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis-dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption.

Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps

Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.

Image denoising in acoustic microscopy using block-matching and 4D filter

Scanning acoustic microscopy (SAM) is a label-free imaging technique used in biomedical imaging, non-destructive testing, and material research to visualize surface and sub-surface structures. In ultrasonic imaging, noises in images can reduce contrast, edge and texture details, and resolution, negatively impacting post-processing algorithms. To reduce the noises in the scanned image, we have employed a 4D block-matching (BM4D) filter that can be used to denoise acoustic volumetric signals. BM4D filter utilizes the transform domain filtering technique with hard thresholding and Wiener filtering stages. The proposed algorithm produces the most suitable denoised output compared to other conventional filtering methods (Gaussian filter, median filter, and Wiener filter) when applied to noisy images. The output from the BM4D-filtered images was compared to the noise level with different conventional filters. Filtered images were qualitatively analyzed using metrics such as structural similarity index matrix (SSIM) and peak signal-to-noise ratio (PSNR). The combined qualitative and quantitative analysis demonstrates that the BM4D technique is the most suitable method for denoising acoustic imaging from the SAM. The proposed block matching filter opens a new avenue in the field of acoustic or photoacoustic image denoising, particularly in scenarios with poor signal-to-noise ratios.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.

Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3mm/3%/10% and 2mm/2%/10%, respectively. The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (>96% for 3mm/3%/10% and>94% for 2mm/2%/10%, respectively) were also observed. A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

Towards quantitative in vivo dosimetry using x-ray acoustic computed tomography.

Radiation dosimetry is essential for radiation therapy (RT) to ensure that radiation dose is accurately delivered to the tumor. Despite its wide use in clinical intervention, the delivered radiation dose can only be planned and verified via simulation. This makes precision radiotherapy challenging while in-line verification of the delivered dose is still absent in the clinic. X-ray-induced acoustic computed tomography (XACT) has recently been proposed as an imaging tool for in vivo dosimetry. Most of the XACT studies focus on localizing the radiation beam. However, it has not been studied for its potential for quantitative dosimetry. The aim of this study was to investigate the feasibility of using XACT for quantitative in vivo dose reconstruction during radiotherapy. Varian Eclipse system was used to generate simulated uniform and wedged 3D radiation field with a size of 4 cm 4 cm. In order to use XACT for quantitative dosimetry measurements, we have deconvoluted the effects of both the x-ray pulse shape and the finite frequency response of the ultrasound detector. We developed a model-based image reconstruction algorithm to quantify radiation dose in vivo using XACT imaging, and universal back-projection (UBP) reconstruction is used as comparison. The reconstructed dose was calibrated before comparing it to the percent depth dose (PDD) profile. Structural similarity index matrix (SSIM) and root mean squared error (RMSE) are used for numeric evaluation. Experimental signals were acquired from 4 cm 4 cm radiation field created by Linear Accelerator (LINAC) at depths of 6, 8, and 10 cm beneath the water surface. The acquired signals were processed before reconstruction to achieve accurate results. Applying model-based reconstruction algorithm with non-negative constraints successfully reconstructed accurate radiation dose in 3D simulation study. The reconstructed dose matches well with the PDD profile after calibration in experiments. The SSIMs between the model-based reconstructions and initial doses are over 85%, and the RMSEs of model-based reconstructions are eight times lower than the UBP reconstructions. We have also shown that XACT images can be displayed as pseudo-color maps of acoustic intensity, which correspond to different radiation doses in the clinic. Our results show that the XACT imaging by model-based reconstruction algorithm is considerably more accurate than the dose reconstructed by UBP algorithm. With proper calibration, XACT is potentially applicable to the clinic for quantitative in vivo dosimetry across a wide range of radiation modalities. In addition, XACT's capability of real-time, volumetric dose imaging seems well-suited for the emerging field of ultrahigh dose rate "FLASH" radiotherapy.

A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system-Fibonacci Q Matrix encryption.

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image's security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method's resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique's average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.

A comparative Study of Image Enhancement Techniques for Natural Images

Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.

Comparison of Gamma Radiation Effects on CCD Cameras Under Different Bias Conditions Using Image Analysis Techniques

In this article, we have investigated gamma radiation effects on two identical CCD cameras under “power on” and “power off” conditions. The cameras were irradiated up to a total ionizing dose of 40 krad at a dose rate of 20 krad/h using a Co-60 gamma source. The images captured at regular intervals with both cameras were analyzed using image quality analysis techniques to evaluate the performance degradation of the cameras with gamma dose. The parameters such as the mean value, mode value, variance, number of bright pixels, peak signal-to-noise ratio, and structural similarity index matrix were calculated from the image data. All statistical parameters showed that for the same gamma dose, the degradation of the camera under the “power on” condition was higher than that of the camera, which was irradiated under the “power off” condition. The image analysis techniques presented in this article could be helpful for assessing the health or useful life of the cameras during operation in nuclear facilities.

Improvising limitations of DNN based ultrasound image reconstruction.

Ultrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniques increases the computational complexity and computational time with improvement in image quality. To overcome these problems, various DNN (Deep Neural Networks) based techniques have been proposed which can reconstruct ultrasound images directly from ultrasound raw data. But DNN implementation has two limitations: accuracy of reconstruction and generalizability of the model. To overcome these limitations, we are proposing methodologies with a DNN model which was able to reduce these limitations. Firstly, we generated the datasets which include multiple shapes such as line, circle, ellipse, and parabola. After that, we have implemented a CNN-DNN (Convolution Neural Network and Deep Neural Network) hybrid model which has significantly improved computational time as well as image quality. We have trained our model with different sets of data to validate the reconstruction of the image matrix. We achieved a significant improvement in computational time of around 100 times (from around 0.6s to 0.0059s) as compared to DAS beamforming technique. At the same time, we also achieved a significant improvement in image quality with 37.19dB average and 41.37dB maximum improved Peak Signal to Noise Ratio (PSNR), and 87.41% average and 95% maximum Structural Similarity Index Matrix (SSIM) value. We also achieved generalizability and precise image reconstruction by using the proposed model.

An efficient segmentation technique for skeletal scintigraphy image based on sharpness index and salp swarm algorithm

Automatic Segmentation of skeletal scintigraphy images is an essential step for diagnosing bone metastasis and allowing definitive treatment. However, automatic segmentation of skeletal scintigraphy images is a challenging task due to the variance of the intensity distribution in different bones of the skeleton. This paper presents a multi-threshold technique to segment the dark spots of scintigraphy images. It segments the skull, the trunk, and the lower limbs separately rather than the whole skeletal scintigraphy image. Firstly, a sharpness index for each of the three parts is evaluated, and then an optimal threshold is computed using the salp swarm algorithm (SSA) with a fitness function based on maximizing the tsallis entropy function for each part. The proposed technique is implemented and applied with several different measures to an Egyptian medical dataset collected from Menoufia University Hospital. It is a real dataset, not standard dataset, that is a significantly addition to the research. It has its problems and drawbacks, which we are working to improve such as the invisible dark parts resulting from different external factors. The proposed technique is compared to the state-of-the-art techniques after applying the collected skeletal scintigraphy dataset to these techniques. Experimental results show that the proposed technique achieves superior performance in almost metrics such as mean square error (MSE), peak signal to noise ratio (PSNR), precision, recall, accuracy, normalized absolute error (NAE), Jaccard index, dice coefficient, matthews correlation coefficient (MCC), F1-Score, and structural similarity index matrix (SSIM).

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

Heritage multimedia, which include photographs, customs, knowledge, arts, rituals, audio, cultural information, and music, are valuable artifacts of any region. The most important attribute of heritage media is the transmission of important features of past generations, which reflect their way of living, innovative attitude, and diversity in archaeological and historical perspectives. However, the proliferation of the Internet has made such data exchange more challenging than ever, allowing unauthorized users to easily access such information. Under such circumstances, securing cultural heritage (CH) media is essential. In that regard, herein, we present a spatial domain-based blind and robust watermarking scheme for the ownership verification of colored CH images; this scheme uses DC coefficient modification. In this scheme, the “Y” element of the YCbCr space is used for inserting a watermark. The “Y” element of a host image is divided into non-overlapping blocks with sizes of 8 × 8. Each 8 × 8 block is then divided into two 4 × 8 subblocks. Instead of calculating the DC coefficients using the discrete cosine transform, we independently calculate the DC coefficient of every 4 × 8 subblock in the spatial domain. We test our method based on standard test images obtained from the USC-SIPI dataset and a self-created dataset of cultural images. Our scheme demonstrates improved robustness and lower computational complexity than frequency-domain-based techniques. The average peak signal-to-noise ratio of the proposed technique for test images is 40.0830 dB, and the structural similarity index matrix value is closer to one under no attack, ensuring the imperceptibility of the technique. Further, we prove the resilience of the proposed algorithm by comparing it with various state-of-the-art techniques.

A Low Area FPGA Implementation of Reversible Gate Encryption with Heterogeneous Key Generation

The reversible computation is the process of designing the architecture with reversible logic gates (RLG) and applicable for optical computing, digital signal processing, nanotechnologies and low-power circuits. In this study, integer wavelet transform (IWT) compression technique is applied to the input image to compress the pixel value. The utilization of the IWT is used to improve the quality of the image in terms of peak signal-to-noise ratio (PSNR) and structural similarity index matrix (SSIM). In addition, different types of RLGs are used to perform the encryption and decryption of the images. A random number is generated using the Lorenz chaotic system (LCS) that contains three different stages and each stage is developed using arithmetic blocks. Here, an effective key value is generated from the input image values by connecting the LCS’s output with the heterogeneous key generation (HKG) module. The inverse IWT (IIWT) technique is used to retrieve the original data during execution of decryption operation. Application specific integrated circuit and field-programmable gate array (FPGA) performances are calculated for reversible logic cryptographic design (RLCD) IWT-HKG architecture. The results showed that it has achieved better performance compared to conventional methods. Moreover, security analyses such as avalanche effect, side channel attack and session key agreement are performed for the RLCD-IWT-HKG method. The RLCD-IWT-HKG method has achieved 89 LUTs, 52 flip flops and 31 slices for the Virtex 6 FPGA device. After retrieving the decrypted images, values of PSNR and SSIM are evaluated as 39.90 dB and 0.6874, respectively.

Monarch butterfly optimization algorithm for computed tomography image segmentation

In the medical field, image segmentation provides important information for surgical planning and registration, and thus demands accurate segmentation. In order to improve the effectiveness and the threshold accuracy of segmentation, researchers have tended to use a metaheuristic algorithm as the operational algorithm to achieve better exploitation and exploration of the search space and to solve many different medical image problems in an effective manner. This research applies the monarch butterfly optimization (MBO) algorithm for image segmentation at multiple threshold values. To evaluate the performance of the implemented MBO algorithm, a comparison is made with the brute-force (i.e. Otsu) algorithm and two metaheuristic algorithms (i.e. Darwinian particle swarm optimization (DPSO) and fractional-order DPSO). In addition, the quality structural similarity index matrix and the peak signal-to-noise ratio are used to evaluate the accuracy of the resultant segmented images. The experimental results show the advantage of using the MBO algorithm for medical image segmentation in terms of accuracy and speed. Regards the accuracy, MBO algorithm produced an exact match at thresholds 1 and 2 and a very close match at thresholds 3 and 4. Regards the speed, the average of the execution time for threshold 1 was 0.24187 s, for threshold 2 was 0.33831 s, for threshold 3 was 0.95967 s and for threshold 4 it was 1.15308 s.

Temporal Masking with Luma Adjusted Interframe Coding for Underwater Exploration Using Acoustic Channel

Transmission of video information from a submerged sensory source may lead to numerous applications such as surveillance, deep sea exploration, monitoring and repairing of underwater oil pipes etc. Such application requires a real time video transmission. It is obviously a challenging task to achieve efficient underwater video transmission because of the limited channel bandwidth and water is not a uniform medium due to which the transmitted signal is more error prone. Due to the limited spectrum, transmitted information must be compressed in a way that its quality does not deteriorate and at the same time, it must achieve low bit rate. In this regard, we propose an algorithm of temporal masking with Luma adjustment to reduce the video information for efficient transmission and reduce the need of over compression. Proposed algorithm is applied on the moving picture expert group (MPEG) video standard. A 2D-least mean squared adaptive filter (LMSAF) is embedded at the receiver end in order to reduce the effect of aquatic noises and further compensate the overall quality. Motion in the video sequence is exploited to extract motion vectors for temporal masking and also to achieve desired bit rate for acoustic applications. Structural Similarity Index Matrix (SSIM) acquired by implementing the proposed algorithm is 0.908 while the standard H.264/AVC was 0.925, but the compression ratio was reduced to 5.03 in comparison to 7.33, which clearly shows the significance of the proposed algorithm in reducing the information size.

C-LIENet: A Multi-Context Low-Light Image Enhancement Network

Enhancement of low-light images is a challenging task due to the impact of low brightness, low contrast, and high noise. The inability to collect natural labeled data intensifies this problem further. Many researchers have attempted to solve this problem using learning-based approaches; however, most models ignore the impact of noise in low-lit images. In this paper, an encoder-decoder architecture, made up of separable convolution layers that solve the issues encountered in low-light image enhancement, is proposed. The architecture is trained end-to-end on a custom low-light image dataset (LID), comprising both clean and noisy images. We introduce a unique multi-context feature extraction module (MC-FEM) where the input first passes through a feature pyramid of dilated separable convolutions for hierarchical-context feature extraction followed by separable convolutions for feature compression. The model is optimized using a novel three-part loss function that focuses on high-level contextual features, structural similarity, and patch-wise local information. We conducted several ablation studies to determine the optimal model for low-light image enhancement under noisy and noiseless conditions. We have used performance metrics like peak-signal-to-noise ratio, structural similarity index matrix, visual information fidelity, and average brightness to demonstrate the superiority of the proposed work against the state-of-the-art algorithms. Qualitative results presented in this paper prove the strength and suitability of our model for real-time applications.

Design of High Accuracy Inexact 4-2 Compressors

Approximate (Inexact) computing is best suitable for the applications where they can accept the results with acceptable loss. We have also introduced three new models of inaccurate 4-2 compressors in this paper, using the current inaccurate 4-2 compressors. All proposed work results are measured in a standard CMOS technology of 90 nm and compared to previously published work. The proposed compressors provide the promised results in terms of error and electrical performance as compared with its counterpart. In order to validate our results, we have also performed image sharpening and image smoothening by using our proposed multipliers. The Structural Similarity Index Matrix (SSIM) of proposed multipliers are measured and interesting results were found.

Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels. The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches. To overcome these drawbacks, the authors propose a novel two-stage framework. In the first step, sub-bands of wavelets of a low-resolution image are obtained using the dual-tree complex wavelet transform. Criminisi algorithm and auto-regression technique are then applied to these sub-bands to inpaint the missing regions. The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast. In the second step, the image is enhanced using super-resolution technique. The process of down-sampling, inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time. The framework is tested on video sequences by comparing and analysing the structural similarity index matrix, peak-signal-to-noise ratio, visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms. The experimental analysis gives visually pleasing results for object removal and error concealment.

Nonsubsampled contourlet transform with cross‐guided bilateral filter for despeckling of medical ultrasound images

AbstractThis paper aims to enhance the image quality in ultrasound images. The significant difficulties in the ultrasound image are the presence of speckle noise. Speckle is the granular noise, and this kind of noise produces a lot of challenges during medical diagnosis. Also, these kinds of problems degrade image quality. The proposed work overcomes this kind of issue with nonsubsampled contourlet domain‐based cross‐guided bilateral filtering. Initially, additive speckle noise is introduced with the log‐transformed Rayleigh distribution. This additive speckle noise component is to be decomposed through the non‐subsampled contourlet domain transform. Then the decomposed image is despeckled by the cross‐guided bilateral filter, and the cost function of this filter is minimized through adaptive galactic swarm optimization. The proposed filtering technique despeckles the additive noise in the noisy image. Finally, the decomposed image is reconstructed using the inverse transform of the nonsubsampled decomposition. The implementation of the proposed methodology is carried out in MATLAB simulation platform. Also, the performance of the proposed work is measured by few metrics such as structural similarity index matrix, mean structural similarity index matrix, mean square error, peak signal to noise ratio, Bhattacharyya coefficient, and speckle index. The result of cost function optimization provides less distortion when compared to other techniques.

Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA

Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a parallel implantation of the same using FPGA is proposed.

Embedding in medical images with contrast enhancement and tamper detection capability

This paper presents an efficient data hiding technique capable of providing improved visual quality of watermarked images, besides having the ability to detect the tamper, if any. It is a spatial domain approach in which major emphasis is on improving the visual quality rather than increasing the PSNR or the embedding capacity. The medical images have been divided into Region of Interest (ROI) and Non-Region of Interest (NROI). Bringing out details that lie within the low dynamic range is very important in medical images for effective diagnosis. ROI being diagnostically critical region is enhanced using contrast stretching and subsequently, data is reversibly embedded into the peak bins of ROI. Only those peak bins are employed for reversible data embedding that have an adjacent empty bin to overcome the problem of overflow and underflow. In NROI, the uniform intensity and redundant information region, Least Significant Bit (LSB) embedding is employed for increasing the payload. For tamper detection, a fragile watermark has been embedded in the ROI. To evaluate the scheme various parameters like peak signal to noise ratio (PSNR), No-Reference Quality Metric for contrast-distorted images (NR-CDIQA) and Structural Similarity Index Matrix (SSIM) have been calculated. The experimental results show a remarkable increase in visual quality compared to state-of-art.