An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising

Finding gradient maximum and local maximum based on multi-scale Canny operator edge detection is really the optimization for two dimension multi-element function.The maximal function gradient or local maximum which was derived by the traditional analytics is approximate and local.A new multi-scale edge detection algorithm is proposed by a genetic optimum searching algorithm.To upgrade the genetic algorithm convergence about edge detection,an improved GA+SA+TABU is used in order to overcome the defects of local searching in the general genetic algorithm and upgrade the whole resolution.Alternating optimization tactics are utilized by combining the general algorithm and heuristic searching methods.The experimental results show that the proposed algorithm applied to IR target image segmentation could result in copious details,single edge and exact location.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Improved multi-scale wavelet in pantograph slide edge detection

This chapter focuses on the development of novel image enhancement and robust edge detection methods for practical medical image processing. It is known that the popular transformation-domain shrinkage approach for image enhancement applies a fixed mathematical basis to transform all images to be processed for noise or artifact reduction. However it is not adaptable to processed images, and then easily leads to blurring in the enhanced images. On the other hand, the techniques that are commonly used for edge detection are known as gradient and Laplacian operators (or mask), and smoothed gradient masks are typically used for edge detection in noisy images. However, these methods share a common major drawback wherein the associated masks are always fixed irrespective of the noise level in the images. In this study, we propose a novel learning-based method to adaptively deduce the transforming basis or masks from the processing data for medical image enhancement and robust edge detection. By using independent component analysis (ICA), the proposed learning-based method can extract suitable basis functions or masks for image transformation for processing data, which are adaptable to both the processed image and related noise in the image. The efficiency of the proposed learning-based method for medical image enhancement and edge detection is demonstrated experimentally using positron emission tomography (PET) and magnetic resonance imaging (MRI) medical images.

As medical images are fuzzy, edge detection based on texture characteristics is comparatively effective than intensity based techniques. A new methodology is described for texture edge detection in medical images that is applicable across modalities. We use a multi-scale filter to capture texture edge information. An experimental prototype based on the proposed methodology provides a test bed for comparison with a popular edge detection technique.

Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data.This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed.

In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

In this paper, we present an edge detection method based on the thin-plate spline with tension. Under regularization theory, the image is represented in a convolution form between the original image data and a two-dimensional kernel. This convolution kernel is derived from a PDE, which is related to a second order (thin-plate or bending) term and a first order (membrane or tension) term. This image representation involves two parameters: a smoothing parameter (or scale parameter) and a weighted smoothness parameter (which controls the degrees of the continuity of the reconstruction by placing a different weighting on the thin-plate and tension terms). By tuning these parameters, the image can be represented at different scales and with different smoothness requirements. Based on this convolution representation, the edges can be detected by differentiating the image to look for the zero-crossings of the Laplacian. By tuning the values of these two parameters, the edges at the different scales will be extracted.KeywordsEdge DetectionCoarse ScaleModify Bessel FunctionNoise SuppressionConvolution KernelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Edge detection plays a crucial role in image processing and computer vision, and is widely used in tasks such as object recognition and image segmentation. Traditional edge detection algorithms perform well in many applications, but there are still some shortcomings in terms of real-time performance and processing efficiency. To address this issue, a highly efficient image edge detection model combining Sobel algorithm and field programmable gate array technology was proposed. YCbCr color space conversion was performed on the image, then Sobel operator was utilized to calculate the image gradient, and adaptive thresholding method was applied to determine the edges. Finally, the model was implemented and optimized on a field programmable gate array. The experimental results showed that when the dataset size was 1000, the information retention rate of the proposed image preprocessing model was 0.89, and the structural information loss was 0.05. When the data volume was 100, the accuracy of the proposed image edge detection model was 0.90, and the root mean square error value was 0.16. The research results indicate that the proposed image edge detection model based on field programmable gate arrays has significant advantages in edge detection performance and processing efficiency. The model has high accuracy and speed in image edge recognition, which can provide certain guidance for research in the field of image edge detection

Color Image Edge Detection Based on Fuzzy Rule Based Approach

Based on mathematical morphology of omni directional multi-scale element, an algorithm of image edge- detection was proposed. Mathematical morphology of omni directional multi-scale element is defined in order to suppress noise and adapt to different edge in the image. An approach of image edge detection based on morphology of omni directional multi-scale element is constructed by power adding combination of morphological operation. The results of simulation in medical image demonstrate that the method performs better not only in edge detection but also in noise-suppression than classical edge detection operator.

In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be—and more and more frequently are—taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approac...

In medical image processing, Magnetic Resonance Imaging (MRI) is one of significant diagnostic techniques. It provides high quality of important information about the analysis of human soft tissue when measured with CT imaging modalities; hence it is suitable for diagnosis at best. However, if it gives quality of information, image may distorted by noise because of image acquisition device and transmission. The noises in MR image reduces the quality of image and also damages the segmentation task which can lead to faulty diagnosis. Noises have to reduce at the same time there is no information loss. This paper propose a hybrid approach to enhance the brain tumor MRI images using combined features of Anisotropic Diffusion Filter (ADF) with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF).ADF scheme provides a superior performance by removing noise while preserving image details and enhancing edges.MDBUTMF helps in image denoising as well as preserving edges satisfactorily when the noise level is high.The performance of this filter is evaluated by carrying out a qualitative comparison of this method with other filters namely, ADF filter, Modified Decision Algorithm, Median filter, MDBUTMF.

Speckle Noise is a natural characteristic of medical ultrasound images. It is a term used for the granular form that appears in B-Scan and can be considered as a kind of multiplicative noise. Speckle Noise reduces the ability of an observer to distinguish fine details in diagnostic testing. It also limits the effective implementation of image processing such as edge detection, segmentation and volume rendering in 3 D. Therefore; treatment methods of speckle noise were sought to improve the image quality and to increase the capacity of diagnostic medical ultrasound images. Such as median filters, Wiener and linear filters (Persona & Malik, SRAD ... ..).The method used in this work is 2-D translation invariant forward wavelet transform, it is used in image processing, including noise reduction applications in medical imaging.