Dental image analysis with different edge detection operators

Progress in deep learning-based dental and maxillofacial image analysis: A systematic review

Wavelet is one method that can be used as a step to recognition an individual . Wavelet is used to perform an image feature extraction . In the process of image texture analysis , showed the value of the coefficients of a wavelet . The magnitude of the coefficients obtained using wavelet feature extraction is influenced by several factors . Besides affected by the type of wavelet used , is also influenced by the magnitude of the wavelet decomposition level of itself. In the wavelet is known as the wavelet energy retention , which means the number of retained energy after undergoing a process of decomposition and cutting coefficients . During the decomposition process , the calculation for texture analysis is often a constraint . In order for the current calculation lighter texture analysis , necessary to the process of cutting coefficients based on the retention factor of the wavelet coefficients . Based on these issues , created a program to analyze the influence of variations in the level of wavelet decomposition and coefficient of variation coefficient of cutting a cut on the image . The object of this final project is 30 iris image that has been converted into polar form presented . Having experienced the process of cutting coefficients , to prevent the image of the reverse transformation has a big difference to the original image , in this study was calculated Mean Square Error ( MSE), Peak Signal to Noise Ratio ( PSNR), and Euclidean distance to determine the level of similarity of image input and output images . The Highest values of Retention ​, MSE and the Euclidean distance is obtained at the level of decomposition of 1 and the lowest at the level of decomposition 6 . While the truncated coefficient and PSNR values ​​obtained at the highest level of decomposition of 6 and the lowest level of decomposition of 6 . The variation coefficient of pieces , value retention and highest PSNR obtained at koefsien piece 5 and the lowest coefficient of 50 pieces . While the coefficient value is truncated , MSE and the highest Euclidean distance is obtained at koefsien pieces 50 and the lowest coefficient of 5 pieces . Keywords: iris, texture analysis, Haar wavelet transform, MSE, PSNR, E uclidean distance

This paper proposes image quality measures based on multifractality preservation. Image quality measures play critical roles in designing and evaluating image processing schemes and performance, especially in which the resulting images deviate from the original ones. Examples of such schemes include image denoising and lossy compression. Traditional quality or distortion measures have been based on mean square error (MSE) measure or its derivatives, e.g., signal to noise ratio (SNR) or peak SNR (PSNR). Although such measures often lead to optimal schemes (with respect to MSE or PSNR), they are known to remove image parts that has noise-like appearances. Furthermore, they treat image singularities such as sharp edges or high textures (that are more important visually and diagnostically) and other image parts (that are less important) uniformly. In contrast, multifractal measures proposed in this paper characterize image singularities. This means the measures pay attention more on important image features, such as sharp edges and high textures. Furthermore, it can distinguish noise from noise-like signals through their differences in their types of singularities. As a result, the measure can be used to assess image quality of sensitive images resulting from processing schemes. The paper shows various ways of defining the measure that reveals multifractality of different aspects of images. It reports the use of the multifractal measure to compare a joint photographic expert group (JPEG) scheme and a variant of differential pulse code modulation (DPCM) coding showing that the DPCM scheme is superior in multifractal preservation for comparable compression ratios. As a result, the DPCM coding has been selected for a database of aerial ortho images.

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.

Currently, image steganography methods are becoming popular in the field of data authentication and image processing. This provides efficient way of communication between sender and receiver without any loss in the originality of the cover image. So, the present paper proposes a novel method called as pattern based image steganography. The proposed method allows the sender to embed the secret message into hierarchical divided sub sections. During the first stage, the input cover image is divided into 25×25 non overlapped windows. Then further each window is divided into 5×5 sub sections. Then among them, the sub sections will be selected based on the pattern `Z'. Next, in the second stage, within each selected 5×5 sub section, the pixels will be selected based on the pattern `a'. Finally, in the selected pixels, 1 bit Least Significant Method (LSB) is used for embedding the message. The proposed method is experimented with nine cover images and the performance of the proposed method is measured with several measures viz., Mean Square Error(MSE), Mean Absolute Error(MAE), Root Mean Square Error(RMSE), Peak Signal to Noise Ratio(PSNR), Signal to Noise Ratio(SNR). The results show the effeciency of the proposed method.

Nowadays understanding of image processing and pattern recognition is the great focus to develop an application of image segmentation. Edge detection is the fundamental technique that can provide information of the boundaries in different objects in an image. Selecting a suitable edge detection algorithm is important to obtain the best performance of segmentation. This paper presents an analysis of implementation for various edge detection techniques on color spaces. We implement Sobel, Prewitt, Robert, and Laplacian edge detection on Hue, Saturation, Value (HSV) and YUV color space by using threshold = 65. We calculated the mean square error (MSE), root mean square error (RMSE), peak signal to noise ratio (PSNR) and execution time as a comparison. This paper tested images from Berkeley Segmentation Dataset (BSDS). The results showed that Robert edge detection in HSV color space especially hue channel can be utilized to obtain more precise boundaries in different objects and it is also quick to compute.

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.

Mammography is a popular modality used by the radiologists to detect the affected areas in human breast. Microcalcification detection is not an easy task because of their small size. This paper proposes a methodology for enhancement of low contrast mammograms and then applying three edge detection operators with K-means clustering and fuzzy C-means clustering to find out microcalcification clusters in a mammogram taken from MIAS database. To validate the work, the proposed methodology is tested on different mammographic images, and the results obtained are compared on the basis of different metrics such as accuracy, mean square error (MSE) and peak signal-to noise ratio (PSNR).KeywordsBreast cancerMammogramBBHEFuzzy C-meansK-means clustering

Reversible image watermarking schemes are used to protect ownership and copyrights of digital images. This paper proposes a novel reversible image watermarking scheme based on adaptive image interpolation, segmentation and additive prediction error expansion (PEE). Proposed interpolation comprises of weighted average of neighboring pixels by allocating higher and lower weights to less and more distant neighboring pixel values respectively. The proposed adaptive image interpolation focuses on detection of edges thus minimizing artifacts imposed by interpolation. The idea of embedding varying amount of watermark bits in different image segments has been explored. Simple linear iterative clustering (SLIC) based image segmentation is performed to separate very sharp, sharp, smooth and very smooth regions in image. Higher number of watermark bits are embedded in sharp regions by using additive prediction error expansion embedding technique. Simulations of proposed and existing techniques were performed on different images and compared using embedding capacity (EC), peak signal to noise ratio (PSNR), computational efficiency, image quality, mean square error (MSE), normalized cross correlation (NCC) and structural similarity index (SSIM). The experimental results show that proposed scheme achieves better results in terms of EC, PSNR, computational efficiency, image quality, MSE, NCC and SSIM as compared to existing techniques.

Digital images contain large amount of information that need evolving effective techniques for storing and transmitting the ever increasing volumes of data. Image compression addresses the problem by reducing the amount of data required to represent a digital image. Discrete Cosine Transformation (DCT), quantization and entropy encoding are the steps in the compression of the JPEG image format. In this proposed work, the performance analysis of JPEG is carried out. Code is written for the Joint Photographic Experts Group (JPEG), the factors Compression Ratio (CR), Signal to Noise Ratio (SNR), Peak SNR (PSNR) and Mean Square Error (MSE) are studied considering different compression ratio for different images. It is found that performance will not remain same for different images even though compression factor was same.

A Study on Development of Optimal Noise Filter Algorithm for Laser Vision System in GMA Welding

Digital watermarking technology is increasingly used to protect copyright and demonstrate ownership of digital multimedia (such as text, music, photos, and videos). In order to safeguard intellectual property rights and rules of ownership for multimedia, this project suggests a text watermark algorithm. The process of hiding little text or grayscale images is the main focus, though. The masking of a watermark text in a high-color or high-density area of the block picture has been proposed using an embedding technique based on an energy function and the Least Significant Bits (LSB) method. Even with various format types and picture sizes chosen to conceal and cover a changing message size, the quality results demonstrate that the watermark image suffers from less distortion than the cover image, and the suggested algorithm is powerful to conceal a random watermark text even with smaller block sizes. An additional optional choice to encrypt the text watermark before embedding is also recommended because doing so would make it harder for hackers to read the text. This text can be encrypted using the Caesar cipher method before embedding is implemented in images.The experimental results of using the suggested algorithm for embedding and extracting watermark text for various sizes in a large number of images were satisfactory, giving a level of peak signal-to-noise ratio (PSNR) and Signal to Noise Ratio (SNR) with low mean square error (MSE) values. However, PSNR degrades more quickly than LBS as the watermark text size increases, so it was determined that it is more suitable for applying a watermark rather than a stego It is employed in order to share information securely

Partial Discharge (PD) signal measurement is very significant tool in analyzing condition of the electrical insulation. The PD information is lost in the presence of various noises. The wavelet transform (WT) based denoising provides a better platform for pre and post processing of PD signal. The wavelet adaptive Thresholding de-noising techniques are well suited for reducing the noise. This paper adopts the various adaptive thresholding techniques such as VisuShrink, SureShrink, combination of the two called Heursure, minimax thresholding and BayesShrink, which are broadly classified as Global and Local thresholding methods. The algorithm presents the comparative analysis for the selection of optimal mother wavelet. Once the optimal mother wavelet is chosen, selection of the best thresholding rule is identified by comparing the values of signal to noise ratio (SNR), mean square error (MSE) and Peak Signal to Noise ratio (PSNR) of all the techniques. The algorithm also presents the comparison between Hard and Soft thresholding. It is shown that the soft thresholding is best suited to remove the noise compared to hard thresholding. The simulated Damped Exponential Pulse (DEP) and Damped Oscillatory Pulse (DOP) has been used. Three sets of PD data are considered to check the performance of the algorithm.

Design and Implementation of Fractional Order Integral Filter for Denoising of Echocardiographic Images