PLIP based unsharp masking for medical image enhancement

Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

Ultrasound imaging has features like non-invasive nature, real time image formation capacity and relatively low cost, which makes this diagnostic tool attractive and hence has become an important imaging modality in medical diagnoses. However the usefulness of this imaging is degraded by the presence of speckle noise. Hence, speckle suppression in ultrasound images is essential for improving the image quality. In this paper, a novel hybrid method is proposed for enhancing the visual quality of medical ultrasound images. To preserve edges, a nonlinear filter is used in the pre-processing stage. Further the pre-processed image is denoised in wavelet domain using bivariate shrinkage. The quality of the despeckled images is assessed using objective quality assessment metrics such as Peak Signal to Noise Ratio (PSNR), Root Mean Squared Error (RMSE) and Edge Preservation Index (EPI). The quantitative results indicate that the proposed method yields better performance than the existing methods.

It is obvious that there is a need for a Medical Decisiveness Determine System (MDDS) should be able to diagnose abnormalities in medical imaging. This is because the medical diagnosis system in health care sectors requires assistants to serve as secondary opinions for medical practitioners. During the process of picture acquisition, it is common practice to adjust the contrast level of medical images in order to prevent image degradation. Contrast enhancement in medical images is typically seen as an optimisation problem, and the Adaptive Genetic Algorithm (AGA) algorithm is utilised in order to arrive at the best possible answer. The findings of the comparison are established between the Adaptive Genetic Algorithm that has been proposed and other algorithms that are already in existence. A number of different performance indicators, including PSNR, SSIM, MSSIM, IFC, VIF, VSNR, MSE, SDME, and NAE, are utilised in order to make comparisons between the results. Methods that have been developed and those that already exist are evaluated using a variety of cancer pictures. As a result, the contrast and quality of medical images can be improved through the utilisation of AGA, which also offers a higher contrast level of medical images, hence facilitating improved decision-making by medical professionals.

This study proposes an improved method based on a nonlinear parametric model for intensity profile to increase the edge localization accuracy in medical images gathered from different imaging modalities. The edge model consists of three parameters associated with an edge point. It also takes into account local background intensity, noise, and blurring. The Marquardt-Levenberg algorithm is used to estimate the parameters because of its accuracy and good convergence rate. Performance of the proposed method is tested quantitatively by comparing the results with those of the well-known active contour method on synthetic vessel images. Qualitative comparisons on real MRI, coronary angiography, CT, ultrasound, and retinal images showed that the proposed method accurately estimates edges in medical images.

Medical imaging help medical doctors provide faster and more efficient diagnoses to their patients. Medical image quality directly influences diagnosis. However, when medical images are acquired, they often present degradations such as poor detail or low contrast. This work presents an algorithm that improves contrast and detail, preserving the natural brightness of medical images. The proposed method is based on multiscale top-hat transform by reconstruction. It extracts multiple features from the image that are then used to enhance the medical image. To quantify the performance of the proposed method, 100 medical images from a public database were used. Experiments show that the proposal improves contrast, introducing less distortion and preserving the average brightness of medical images.

In medical ultrasound B-mode imaging, by increasing the image contrast, spatial and temporal resolution, it will help to improve the diagnostic and decision making process. Previously the unsharp masking (UM) techniques have been successfully implemented for digital image processing as edge enhancement techniques. However, the outcomes of the method are limited and influenced by the image formats conversion and lower dynamic range. Moreover the conventional UM technique sensitive to noise due to the presence of linear high pass filter which cannot discriminate signal from noise. Furthermore, the technique only enhances the image in darker compared to lighter regions. To overcome those issues, a new improved UM method compatible with ultrasound B-mode imaging was applied to compound plane wave imaging (CPWI). The received radio frequency (RF) signal was beamformed with delay and sum (DAS) and filtered delay and sum (FDMAS). Results show that the proposed techniques are not only able to improve the image contrast but also the spatial resolution. The UM technique manages to improve the B-mode image lateral resolution (LR) by 18.36% and 10.25% as well as reduced the peak side lobes (PSL) by 16.67 dB and 21.24 dB on DAS and FDMAS respectively when compounded with CPWI, N=13. For the same number of compounding and beamforming order, the image contrast ratio (CR) has been improved by 10.35 dB and 7.39 dB accordingly.

Contrast enhancement methods based on reversible data hiding (RDHCE) can be used for contrast enhancement of medical images, which is a hot research topic in recent years. However, the region of interest (ROI) of medical images cannot be accurately segmented using the current RDHCE algorithms and histogram pixels clustering in medical images results in incorrect localization of longer intervals, which affects the contrast enhancement effect of images. In this paper, the Unet3+ network model is used, which makes the segmented ROI region and ROI histogram clearer and more accurate than those obtained by the traditional segmentation methods and the algorithm integrates a larger embedding capacity and a better visual quality of the image. It adaptively determines and stretches the interval of the ROI greyscale histogram and at the same time enlarges the embedding capacity of the ROI to enhance the contrast of the image. The proposed algorithm improves the visual quality of medical images by 20% and enhances ROI embedding capacity by 25% compared to existing methods.

The subject of this study is accurate estimation of object edges in medical images which is gathered from different modalities. To obtain edges, a parametric edge model was utilized. The parametric model is not a linear model therefore, in order to determine the parameters of the model a nonlinear estimation algorithm, Marquardt-Levenberg algorithm, was used. Performance of the proposed method is tested on real medical images.

Technical limitations in image capturing usually impose defective, such as contrast degradation. There are different approaches to improve the contrast of an image. Among the exiting approaches, un-sharp masking is a popular method due to its simplicity in implementation and computation. There is an important parameter in un-sharp masking, named gain factor, which affects the quality of the enhanced image. In this paper, a new adaptive un-sharp masking method is proposed. In the proposed method gradient variation of the image is used to estimate the gain factor for un-sharp masking. Gradient variation of an image can provide information about the image contrast. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed un-sharp masking methods. The experimental results show the superiority of the proposed method compared to the existing methods in image enhancing using un-sharp masking.

In the process of medical image formation, the medical image is often interfered by various factors, and it is deteriorated by some new noise that may reduce the quality of the obtained image, which affect the clinical diagnosis seriously. A new medical image enhancement method is proposed in this article. Firstly, the initial medical image is decomposed into the NSCT domain with a low-frequency sub-band, and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. An adaptive thresholding method is used for denoising the coefficients of the high-frequency sub-bands. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 199–205, 2015

Image resolution enhancement is a problem of considerable interest in all medical imaging modalities. Unlike general purpose imaging or video processing, for a very long time, medical image resolution enhancement has been based on optimization of the imaging devices. Although some recent works purport to deal with image postprocessing, much remains to be done regarding medical image enhancement via postprocessing, especially in ultrasound imaging. We face a resolution improvement issue in the case of medical ultrasound imaging. We propose to investigate this problem using multidimensional autoregressive (AR) models. Noting that the estimation of the envelope of an ultrasound radio frequency (RF) signal is very similar to the estimation of classical Fourier-based power spectrum estimation, we theoretically show that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly. Here, this is done by means of a technique that simultaneously estimates the order and the parameters of a multidimensional model using relevant regression matrix factorization. Doing so, the proposed method specifically fits ultrasound imaging and provides an estimated envelope. Moreover, an expression that links the theoretical image resolution to both the image acquisition features (such as the point spread function) and a postprocessing feature (the AR model) order is derived. The overall contribution of this work is threefold. First, it allows for automatic resolution improvement. Through a simple model and without any specific manual algorithmic parameter tuning, as is used in common methods, the proposed technique simply and exclusively uses the ultrasound RF signal as input and provides the improved B-mode as output. Second, it allows for the a priori prediction of the improvement in resolution via the knowledge of the parametric model order before actual processing. Finally, to achieve the previous goal, while classical parametric methods would first estimate the model order and then the model parameters, our approach estimates the model parameters and the order simultaneously. The effectiveness of the methodology is validated using two-dimensional synthetic and in vivo data. We show that, compared to other techniques, our method provides better results from a qualitative and a quantitative viewpoint.

The Ray Casting algorithm plays an important role in improving medical images and edge detection. Medical imaging typically includes a large amount of image data, such as computed tomography, magnetic resonance imaging, etc. These data need to be effectively and accurately processed and analyzed in order for doctors to make correct diagnoses. The Ray Casting algorithm is a method that allows for effective and accurate processing of medical images. This article mainly used experimental methods and algorithm comparison to analyze the running time and accuracy of medical images using different algorithms. The experimental results showed that the image detection accuracy using the improved Ray Casting algorithm exceeds 95%, which can achieve the enhancement and edge detection of precise medical images, and the effect is better than traditional image processing algorithms. Ray Casting Algorithms can quickly and accurately render medical images, allowing doctors and researchers to better understand and analyze image information. Through the Ray algorithm, the voxel-level enhancement and operation of the medical image are realized to improve the contrast and clarity of the image. To detect and extract edge information in medical images to help assist diagnosis and disease analysis.

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.

One of the most important preprocessing techniques is image Contrast Enhancement which is a technique that improve visual quality of an input image becoming more suitable for human analysis and perception. Numerous researches have been already developed for enhancement of Medical image for their important application, Traditional Gamma Correction is found to be one of the simplest technique for the contrast enhancement of medical image. This technique uses a set of varying parameter (y, c) which adjust effectively gray value intensity of the input image. Otherwise, Adaptive Gamma Correction technique have been appeared and have been proved its effectiveness by the use of adaptive (y, c) parameter which are determined adaptively from statistical information of input image. Traditional Gamma Correction (TGC) and Adaptive Gamma Correction (AGC) have been applied on three brain MRI modalities for patient with Multiple Sclerosis pathology. Qualitative and quantitative results are presented to illustrate the comparison of TGC and AGC to enhance the contrast of MRI images for better appearance of normal tissue and diseased tissue affected by MS pathology.

The characterisation of edges in medical images is critical for disease diagnosis. However, existing systems are still deficient in this task. Traditionally, integer-based derivative operators are employed due to their efficiency in time complexity but lack the ability to track nonlocal and non-singular edge maps. This study proposes a new mask based on Atangana-Beleanu fractional operator. This operator has the same complexity as the state-of-the-art integer-order derivative mask known as the Canny edge detector but has the added advantage to characterise more efficiently nonlocal and nonsingular edge maps. Performance evaluation of the proposed mask reveals an enhanced performance in the context of robustness to noise and quality edge extraction, a significant contribution to literature. The metric for the study is the signal-to-noise ratio, and the structural similarity index and appropriate mask observed is a mask of dimension greater than five.