Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function.

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.

Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.

Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

Effective image fusion strategies in scientific signal processing disciplines: Application to cancer and carcinoma treatment planning.

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.

Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection.

BACKGROUND: Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance. OBJECTIVE: The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images. METHODS: The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm. RESULTS: The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts. CONCLUSION: The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.

Fusion of Multimodal Medical Images Based on Fine-Grained Saliency and Anisotropic Diffusion Filter.

BACKGROUND: A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions. OBJECTIVE: Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image. METHOD: In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms. RESULTS: The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency. CONCLUSION: Furthermore, it provides a roadmap for the direction of future research.

The Evolution of Medical Imaging in the Therapeutics of Patients with Skin Cancer

INTRODUCTION: Medical imaging mechanization has reformed medical management, empowering doctors to recognize cancer prematurely and promote patient outcomes. Imaging tests are of significant influence in the detection and supervision of cancer patients. Cancer recognition generally necessitates imaging studies that, in most instances, utilize a trivial amount of radiation. Methodologies such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are predominant in clinical managerial, incorporating remedy and research. BACKGROUND: Over recent years, diagnostic imaging has progressed from a state of commencement to an advanced level. Numerous modern imaging procedures have evolved. Although contemporary medical imaging comprises image exhibition together with image refining, computer-aided diagnosis (CAD), image inscribing and conserving, and image transference, the majority of which are embraced in picture documentation and communication processes. AIM: This review targets to encapsulate toxicology information on skin cancer unpredictability essential to interpretation measures, report important factor that helps in defining skin cancer condition, and possible medical care alternatives or medical attention endorsed referring to diverse aspects involving the size and site of malignancy, the complications, patient's priority and well being. We concisely review various therapy alternatives, methods of radiation autoimmunity, prime observational study designs of medical and distinct radiation resources and cancer risks, and current analysis methodologies and research precision. CONCLUSION: The detail of this paper covers a brief review of research and evolution in medical imaging discipline and mechanism. This review considers the physiology of melanocytes and the pathogenesis of skin cancer using medical imaging. Also, a description of risk factors, prevention methods, screening, various diagnosis methods and different stages of skin cancer, sub-types and different types of treatment methods is provided in this paper for research and development.

Dispersive Modeling of Normal and Cancerous Cervical Cell Responses to Nanosecond Electric Fields in Reversible Electroporation Using a Drift-Step Rectifier Diode Generator.

This paper creates an approximate three-dimensional model for normal and cancerous cervical cells using image processing and computer-aided design (CAD) tools. The model is then exposed to low-frequency electric pulses to verify the work with experimental data. The transmembrane potential, pore density, and pore radius evolution are analyzed. This work adds a study of the electrodeformation of cells under an electric field to investigate cytoskeleton integrity. The Maxwell stress tensor is calculated for the dispersive bi-lipid layer plasma membrane. The solid displacement is calculated under electric stress to observe cytoskeleton integrity. After verifying the results with previous experiments, the cells are exposed to a nanosecond pulsed electric field. The nanosecond pulse is applied using a drift-step rectifier diode (DSRD)-based generator circuit. The cells' transmembrane voltage (TMV), pore density, pore radius evolution, displacement of the membrane under electric stress, and strain energy are calculated. A thermal analysis of the cells under a nanosecond pulse is also carried out to prove that it constitutes a non-thermal process. The results showed differences in normal and cancerous cell responses to electric pulses due to changes in morphology and differences in the cells' electrical and mechanical properties. This work is a model-driven microdosimetry method that could be used for diagnostic and therapeutic purposes.

A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing.

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.

A Survey on Machine Learning Based Medical Assistive Systems in Current Oncological Sciences.

BACKGROUND: Cancer is one of the life-threatening diseases which is affecting a large number of population worldwide. Cancer cells multiply inside the body without showing much symptoms on the surface of the skin, thereby making it difficult to predict and detect the onset of the disease. Many organizations are working towards automating the process of cancer detection with minimal false detection rates. INTRODUCTION: The machine learning algorithms serve to be a promising alternative to support health care practitioners to rule out the disease and predict the growth with various imaging and statistical analysis tools. Medical practitioners are utilizing the output of these algorithms to diagnose and design the course of treatment. These algorithms are capable of finding out the risk level of the patient and can reduce the mortality rate concerning cancer disease. METHOD: This article presents the existing state of art techniques for identifying cancer affecting human organs based on machine learning models. The supported set of imaging operations is also elaborated for each type of cancer. CONCLUSION: The CAD tools are the aid for the diagnostic radiologists for preliminary investigations and detecting the nature of tumor cells.

Comparative evaluation of platelet-rich plasma and guided tissue regeneration membrane in the healing of apicomarginal defects: a clinical study.

INTRODUCTION: The aim of the study was to compare the healing responses of platelet-rich plasma (PRP), PRP + a collagen sponge, and a collagen membrane used as guided tissue regeneration (GTR) materials for the treatment of apicomarginal defects. METHODS: Thirty patients with suppurative chronic apical periodontitis and apicomarginal communication were selected and allocated randomly into three groups according to the barrier technique to be used during periradicular surgery: the collagen membrane group, the PRP group, and the PRP + collagen sponge group. Clinical and radiographic measurements were determined at baseline and every 3 months after surgery up to 1 year. Cases were defined as healed when no clinical signs or symptoms were present, and radiographs showed complete or incomplete (scar tissue) healing of previous radiolucencies. RESULTS: The PRP and PRP + collagen sponge groups depicted 83.33% and 88.89% healing, respectively, in terms of combined clinical-radiographic healing as compared with 80% in the collagen membrane group. All the three treatments showed highly significant (P < .05) reductions in the periodontal pocket depth (PD), the clinical attachment level (CAL), the gingival margin position (GMP), the size of the periapical lesion, the percentage reduction of the periapical rarefactions, and periapical healing. No significant differences between the three groups were evident for these parameters (P > .05). CONCLUSIONS: GTR applied to apicomarginal defects using PRP or PRP + collagen sponge lead to similar enhancements of the clinical outcome of periradicular surgery in terms of periapical healing, gain of periodontal support, PD reduction, and PRP may be an alternative treatment for GTR membrane in the treatment of apicomarginal defects.