Search

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts. [ABSTRACT FROM AUTHOR]

Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral filter (CBF) that utilizes one image to determine the kernel and the other for filtering, and vice versa, by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges. The outputs of CBF are then subtracted from the original images to obtain detailed images. It further proposes to use edge-preserving processing that combines linear low pass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties. These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size. The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges. In addition, weight computations are performed using these reconstructed images, and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details. Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing, well-known algorithms objectively to validate the fusion results. Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation. In addition, the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy. Nevertheless, due to the lower complexity of the fusion algorithm, the efficiency of fusion methods is high in practical applications. The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrast. [ABSTRACT FROM AUTHOR]

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%-20% every year. The wheat rust diseases are identified either through experienced evaluators or computer-assisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed system, a total of 2300 secondary source images were augmented through flipping, cropping, and rotation techniques. The augmented images are preprocessed by histogram equalization. As a result, preprocessed images have been applied to region extraction convolutional neural networks (RCNN); Fast-RCNN, Faster-RCNN, and Mask-RCNN models for wheat plant patch extraction. Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model. As a result, the Gaussian kernel function in CE-SVM achieves high F1-score (88.43%) and accuracy (93.60%) for wheat stripe rust disease classification. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images. We propose an innovative constructive non-local image filtering algorithm by means of applications in low-dose computed tomography technology. The nonlocal mean filter that was recently proposed was modified to construct our denoising algorithm. It constructs the discrete property of neighboring filtering to enable rapid vectorized and parallel implantation in contemporary shared memory computer platforms while simultaneously decreases computing complexity. Subsequently, the proposed method performs faster computation compared to a non-vectorized and serial implementation in terms of speed and scales linearly with image dimension. In addition, the morphological residual processing is employed for the purpose of edge-preserving image processing. It combines linear lowpass filtering with a nonlinear technique that enables the extraction of meaningful regions where edges could be preserved while removing residual artifacts from the images. Experimental results demonstrate that the proposed algorithm preserves more textural and structural features while reducing noise, enhances edges and significantly improves image quality more effectively. The proposed research article obtains better results both qualitatively and quantitively when compared to other comparative algorithms on publicly accessible datasets. [ABSTRACT FROM AUTHOR]

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information. The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details. NSCT is utilized to further decompose these images into their low and high-frequency coefficients. These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve (PCA/KL) based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results. An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrastenhanced fusion coefficients. Finally, an inverse NSCT is applied to each coefficient to produce the final fusion result. Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health. Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

The use of low-dose computed tomography (LDCT) in medical practice can effectively reduce the radiation risk of patients, but it may increase noise and artefacts, which can compromise diagnostic information. In this paper, we propose a LDCT denoising method based on sparse 3d transformation with probabilistic non-local means (PNLM). The hard-thresholding module in the sparse 3d transformation framework is used to attenuate noise in the transform coefficients. In addition, the PNLM overcomes the incompetence of non-local means weights problems where its weights better reflect the patch similarities, thereupon it connects the denoising process and the noise type and thus able to denoise complex noise present in LDCT images. Besides, a significant denoising improvement is obtained by using the collaborative wiener filtering. Experiments on NIH-AAPM Mayo Clinic LDCT dataset show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper. [ABSTRACT FROM AUTHOR]

Summary: High quality medical images are expected for the detailed analysis in medical diagnostic system. However, spatial resolution of medical images mostly suffers from the factors such as medical equipment and time constraints. Despite these limitations, a well‐designed super‐resolution (SR) algorithm will help to improve the resolution of the medical images for medical diagnosis. When comparing to upgrade medical equipment, the adaptation of SR algorithms as a post‐processing method after medical imaging gives the benefits of lower cost and superior performance. This article proposes a piecewise linear regression system for learning image specific low‐to‐high resolution mapping via domain transform filtering and weighted least squares (WLS) optimization framework. We initially employed a WLS optimization framework for gradually coarsen the original input images and extract the multi‐scale information by constructing edge‐preserving multi‐scale decompositions. Then, with the aim of adequately preserving the edges of the medical images, we utilize a recursive filtering in transform domain. The Hadamard patterns generated from low‐resolution training patches are then used to classify the LR–HR training patch pairs into different classes. In the end, the piecewise linear regression system is utilized to learn the mapping relationship between LR space to HR space for each class, which is subsequently utilized to obtain desired HR image. In the context of quantitative metrices and qualitative analysis, our proposed method generates high‐quality medical images as compares to other existing state‐of‐the‐art SR methods. [ABSTRACT FROM AUTHOR]

As pixel resolution continues to increase in images acquired from different sensors, the measurement of the correlation between pixels and their properties requires numerous processing steps over multiple iterations. Quantum image processing (QIP) is a technology capable of processing exponentially large volumes of data within polynomial time. One such application is image fusion. In the field of radiography, computerized tomography (CT) images provide the structural information of bone tissues, and magnetic resonance MR images can provide the visualization of gray‐scale anatomical structures of soft tissues. With the help of CT and MR fusion, a composite image can be obtained containing information on both hard and soft tissues. Clinicians often have to switch between these modalities to trace various patterns for effective staging and diagnosis of various oncological diseases. In this study, a multilevel filtering‐based image fusion algorithm that efficiently fuses information from CT and MR images into a single image has been proposed. First, source images are processed with a rolling guidance filter, and the detail layer is calculated. The base layer is then computed using guided filtering to ensure a high level of edge preservation. Furthermore, to minimize noise and artifacts, the detail layers are fused using the Karhunen–Loeve transform, and the base layers are combined using the weighted superimposition principle. Experimental results demonstrate that the proposed methodology is subjectively more efficient than the existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted. [ABSTRACT FROM AUTHOR]

It is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denoising. In this paper, an efficient denoising scheme using an innovative method of calculating the image base and details is being proposed. The noisy image is thresholded to remove extra noise by the bitonic filter. Details of the discontinuities is extracted by subtracting the recovered image from the noisy image. Subsequently, details features are subtracted from the noisy image to extract the base information. After that, image features and noise are simultaneously filtered by rolling guidance filter to remove the remaining noise from the features and the significant edge information from the filtered noise. The two images are fused with maximum coefficient value to enhance the information content and visual quality of denoised image. The proposed Dual Way Residue Noise Thresholding (DWEFD) is a combination of various spatial and transform domain commutations performed parallelly. Extensive experimental results and investigations reveal that the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal. [ABSTRACT FROM AUTHOR]

Objective. Frequent monitoring of haemoglobin concentration is highly recommended by physicians to diagnose anaemia and polycythemia vera. Moreover, other conditions that also demand assessment of haemoglobin are blood loss, before blood donation, during pregnancy, and preoperative, perioperative and postoperative conditions. The cyanmethemoglobin/haemiglobincyanide method, portable haemoglobinometers and haematology analyzers are some of the standard methods used to diagnose the aforementioned ailments. However, discomfort, delay and risk of infection are typical limitations of traditional measuring solutions. These limitations create the necessity to develop a non-invasive haemoglobin monitoring technique for a better lifestyle. Approach. Various methods and products have already been developed and are popular due to their non-invasiveness; however, invasive solutions are still considered as the reference standard method. Therefore, this review summarizes the attributes of existing non-invasive solutions. These attributes are finalized as brief details, accuracy, optimal benefits and research challenges for exploring potential gaps, advancements and possibilities to consider as futuristic alternative methodologies. Main results. Non-invasive total haemoglobin assessment techniques are mainly based on optical spectroscopy (reflectance/transmittance) or digital photography, or spectroscopic imaging in spot-check/continuous monitoring mode. In all these techniques, we have noticed that there is a need to consider different light conditions, motion artefacts, melanocytes, other blood constituents, smoking and precise fixing of the sensor from the sensing spot for exact formulation. Significance. Moreover, based on careful and critical analysis of outcomes, none of these techniques or products are used independently or intended to replace invasive laboratory testing. Therefore, there is a requirement for a more accurate technique that can eliminate the requirement for blood samples and likely end up as a reference standard method. [ABSTRACT FROM AUTHOR]

Image fusion has emerged as a major area of research in the past few decades due to its extended applications. While progressing in the field of image fusion, a large number of techniques‐based image transforms and spatial filters have been devised for both general and specific sets of images. The primary criterion of image fusion technique is to deliver high‐quality visual perception besides giving a considerable objective evaluation rate. In this study, an information fusion rate‐based study is done on recent, most researched, and high‐performing state‐of‐the‐art techniques using visible and infrared image datasets. These techniques have been chosen carefully, owing to their superiority in performance on both objective and subjective scales of evaluation and have been discussed in terms of their respective advantages and disadvantages. It is clearly evident that some rather primitive techniques can perform well as well as techniques based on a hybrid of various domains can significantly boost the information fusion rate. [ABSTRACT FROM AUTHOR]

Image fusion is important medical image processing technique which aims at fusing complimentary information between two images. A little literature has been found on pre-processing of the source images prior to fusion which directly affects the image fusion quality. In this paper we have proposed an effective image fusion method based on IHS-wavelet transform along with pre-processing of source image in a selected sequence of spatial and transform domain techniques before fusion to create a highly informative fused image. The proposed methodology has outperformed six other state-of-the-art image fusion techniques both in terms of objective evaluation and visual results on osseous and vascular 2-D data [ABSTRACT FROM AUTHOR]

It is extensively endorsed that preserving the intrinsic geometrical features of an image is essential while denoising it. With an aim to achieve this several directional image representations have been given in the recent literature. In this paper an efficient denoising scheme using an innovative method of calculating the residue image is being proposed. The residue image is further thresholded to remove excessive noise while recovering fine features and details. The recovered features are added to first stage of denoised image to enhance the information content and visual quality of denoised image. The proposed methodology DWRNT (Dual Way Residue Noise Thresholding) is a combination of various spatial and transforms domain methods. Extensive experimental results and investigations reveal that our method can depict far better performance in terms of both subjective evaluation and objective evaluation than various other state-of-the-art image denoising techniques. In this way the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures. [ABSTRACT FROM AUTHOR]

Medical imaging is playing a pivotal role in the domain of medical diagnosis. But the major problems related to medical images is that most of the images (of different modalities) are suffered from noise and other quality‐related problems such as poor contrast, blurring, and difficulties in extracting appropriate information. Therefore, it is necessary to construct some techniques that could improve medical images in such a way that it will be ideal for diagnostic applications. Medical image enhancement is the process of increasing the contrast quality of intensity variations and improves the visual representation of medical images. To provide suitable interpretation and clearer image for the observers with reduced noise levels, this article proposes a novel competent medical image enhancement method based on morphologically processing of residuals using a special kernel. First, it combines linear low pass filtering with nonlinear technique that allows for selection of essential regions where edges will get well preserved. The selection of those regions is based on morphological processing of linear filter residuals and then aims to find significant regions specified by edges of high amplitude and appropriate size. The reconstructed regions are combined with the output of low pass filtering to recovers the original shape of edges. In addition, the method allows to control the contrast to avoid blurring while preserving significant image information. In the end, a special kernel is convolved with an image to obtain the sharper image. Experiments on the different types of medical images are performed and the results are compared using different standard evaluation metrics. The quantitative results obtained through proposed method for all the metrics are optimal compared to other competing methods. It is observed that the results on various test images affirm that the proposed method generates excellent image visual quality and significantly enhances the contrast of all images which helps with better diagnosis and treatment. [ABSTRACT FROM AUTHOR]