A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

Design of Pill Filming System and Automatic Pill Classification System

Background Invent Pill classification system that can detect and classify into one tablet unit by deep-learning and Pill filming system that generate comprehensive and multi-dimensional data for learning.Methods Pill filming system and Pill classification system, they have two chapters consisted of structure design, model introduction, and controller design. Pill classification system's structure categorization is Input box, Linear conveyor, and Output box. In Data preprocessing, a similarity map is obtained with Structure Similarity Index Measure(SSIM). And RetinaNet is used as a pill classification learning model. Mean Accuracy Precision (mAP) is 0.9842, and we take experiment about measuring the number and accuracy of the classified pills for each experimenter's classification time. Conclusion Pill filming system and Pill classification system are expected to reduce labour losses for simple tasks. It helps medical personnel focus on significant and urgent tasks. And It can contribute to experiments about that deep-learning control the mechanical device.

Enhancement of MRI Brain Images Using Notch Filter Based on Discrete Wavelet Transform

In this work, we have proposed Notch filter method to enhance MRI brain images. The proposed method performs better when compared with the existing methods from the literature. The performance is evaluated using quantitative measures like Michelon Contrast (MC), entropy, Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index Measurement (SSIM) and Absolute Mean Brightness Error (AMBE), as a parameter on publically available BRATS-2018 & 2019 dataset. Overall, the proposed method performs well in comparison to the other existing methods.

Dynamic Steganography Least Significant Bit with Stretch on Pixels Neighborhood

Background: The confidentiality of a message may at times be compromised. Steganography can hide such a message in certain media. Steganographic media such as digital images have many pixels that can accommodate secret messages. However, the length of secret messages may not match with the number of image pixels so the messages cannot be inserted into the digital images.Objective: This research aims to see the dynamics between an image size and a secret message’s length in order to prevent out of range messages entered in an image.Methods: This research will combine the Least Significant Bit (LSB) method and the Stretch technique in hiding secret messages. The LSB method uses the 8th bit to hide secret messages. The Stretch technique dynamically enlarges the image size according to the length of the secret messages. Images will be enlarged horizontally on the rightmost image pixel block until n blocks of image pixels.Results: This study compares an original image size and a stego image size and examines a secret message’s length that can be accommodated by the stego image, as well as the Mean Square Error and Structure Similarity Index. The test is done by comparing the size change of the original image with the stego image from the Stretch results, where each original image tested always changes dynamically according to the increasing number of secret message characters. From the MSE and SSIM test results, the success was only with the first image, while the second image to the fourth image remained erroneous because they also did not have the same resolution.Conclusion:The combination of LSB steganography and the Stretch technique can enlarge an image automatically according to the number of secret messages to be inserted. For further research development, image stretch must not only be done horizontally but also vertically.

Hybrid Patch Propagation and Diffusion Technique for Image Inpainting

This paper deals with anovel hybrid technique based onpatch propagation and diffusion method for image inpainting. The presented technique is used to reconstruct a damaged image. Image inpainting is a method to fill the hole with the best plausible, which is created due to damage. The novel hybridization technique of diffusion-based and exemplar-based is presented to overcome the existing problem of inpainting. The method is tested on the image dataset of TUM-IID. The performance of present method is measured using quality factor (QF) analysis, peak signal-noise ratio (PSNR), and comparing similarity by structure similarity index (SSIM). Result demonstrates that the proposed calculation performed better contrast with the existing exemplar-based technique.

Image Enhancement and Feature Extraction of Cytomegalovirus Image using Hierarchical Ranking Convolutional Neural Network (HR-CNN)

Human Cytomegalovirus is becoming a common issue around the globe , mainly it deals with the infection of the fetus in the womb. Digital image processing plays a vital role in various fields especially in the field of medicine to have a better quality of image of viruses in various forms. To have better clarity of images even in microscopic images there might be some flaws in detection of viruses because of the intensities which occur due to atmospheric lights, to overcome the flaws in microscopic images there comes a technique image enhancement to overcome noise in images especially distortion free images to be produced based on some image quality assessment and to reduce noise in an image without any loss of information. In this paper the proposed methodology called Hierarchical Ranking Convolution Neural Network is introduced based on Upward/Downward hierarchy and Forward/Backward Hierarchy to extract features and to provide intensified image of the virus. Image quality assessment is done with the parameters and evaluated using Mean Square Error, Peak signal to Noise Ratio, Root Mean Square Error, Structure Similarity Index, Mean Structure Similarity Index to prove the accuracy.

Large Eddy Simulation of an N-Dodecane Spray Flame Under Different Ambient Oxygen Conditions

An n-dodecane spray flame was simulated using a dynamic structure large eddy simulation (LES) model coupled with a detailed chemistry combustion model to understand the ignition processes and the quasi-steady state flame structures. This study focuses on the effect of different ambient oxygen concentrations, 13%, 15% and 21% at an ambient temperature of 900 K and an ambient density of 22.8 kg/m3, which are typical diesel-engine relevant conditions with different levels of exhaust gas recirculation (EGR). The liquid spray was treated with a traditional Lagrangian method. A 103-species skeletal mechanism was used for the n-dodecane chemical kinetic model. It is observed that the main ignitions occur in rich mixture and the flames are thickened around 35 to 40 mm off the spray axis due to the enhanced turbulence induced by the strong recirculation upstream, just behind the head of the flames at different oxygen concentrations. At 1 ms after the start of injection, the soot production is dominated by the broader region of high temperature in rich mixture instead of the stronger oxidation of the high peak temperature. Multiple realizations were performed for the 15% O2 condition to understand the realization to realization variation and to establish best practices for ensemble-averaging diesel spray flames. Two indexes are defined. The structure-similarity index analysis suggests at least 5 realizations are needed to obtain 99% similarity for mixture fraction if the average of 16 realizations are used as the target at 0.8 ms. However, this scenario may be different for different scalars of interest. It is found that 6 realizations would be enough to reach 99% of similarity for temperature, while 8 and 14 realizations are required to achieve 99% similarity for soot and OH mass fraction, respectively. Similar findings are noticed at 1 ms. More realizations are needed for the magnitude-similarity index for the similar level of similarity as the structure-similarity index.

Image Denoising via Asymptotic Nonlocal Filtering

The nonlocal means algorithm is widely used in image denoising, but this algorithm does not work well for high-intensity noise. To overcome this shortcoming, we establish a coupled iterative nonlocal means model in this paper. Considering the computation complexity of the new model, we realize it by using multiscale wavelet transform and propose an asymptotic nonlocal filtering algorithm which can reduce the influence of noise on similarity estimation and computation complexity. Moreover, we build a new nonlocal weight function based on the structure similarity index. Simulation results indicate that the proposed approach cannot only remove the noise but also preserve the structure of image and has good visual effects, especially for highly degenerated images.