×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerIn this work we performed a review regarding the improvement of microscopical optical imaging assisted by using microspheres, focusing on the most recent technologies. We have been reviewed the utilizing of the superlens and nanojet concepts in order to understand the working principles of microspheres in terms of magnification and resolution improvement. Some researches about the parameters effecting on microsphere imaging technique have been presented including the effect of microsphere’s material and size, the effect of immersion medium, and the plasmonic layer effect. Additionally, some applications that serve from this technique have been illustrated.
Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
Cascade single mode-No Core - single mode fiber structure (SNS) optical filters have garnered a lot of interest as dependable optical devices. These devices' simplicity, compactness, affordability, all-fiber design, low transmission loss, and ability to continuously adjust the laser wavelength at a particular spectral range contribute to their dependability. The operation's foundation is multimode interference (MMI) and self-image phenomena. SNS filter based on optimized 4th self-imaging condition for different NCF- Specifications was theoretically optimized a tunable filter based on a cascade single mode-no core-single mode (SNS) fiber structure encircled by Ferrofluid was experimentally investigated. The findings indicate that reducing the NCF diameter can enhance the filter's tunability. device has applications in fiber laser technology, spectroscopy, and optical communication.
Recently, three-dimensional models 3DM in the prosthetics field gained popularity, especially in the context of residual limb shape creation resulting from collecting medical images in Digital Imaging and Communications in Medicine DICOM format from a magnetic resonance imaging MRI after image processing accurately. In this study, a three-dimensional model of the residual limb for a patient with transtibial amputation was realized with the integration of artificial intelligence and a computer vision approach demonstrating the benefits of AI segmentation tools and artificial algorithms to generate higher accuracy three-dimensional model before prosthetic socket design or in case of comparison the 3D model generated from MRI with another 3D model generated from another technique, where a residual limb of a 23 years old male patient with amputation in the left leg wearing a prosthetic socket liner, and having 62 kg weight, 168 cm height, with high activity level. The patient was scanned using GE Medical Systems, 1,5 Tesla Signa Excite. MRI images in DICOM format were read to retrieve essential metadata such as pixel spacing and slice thickness. These images were processed to obtain a model that reflects the real shape of the residual limb using a specific algorithm, and the 3D model was extracted using AI segmentation tools. The obtained 3D model result with high resolution proves the potential of the artificial intelligence approach with deep learning to reconstruct 3D models concluding that AI has an instrumental role in medical image analysis, particularly in the areas of organ and tissue classification and segmentation., thus generating automatic and repetitive a 3D model.
Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases. In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.
The main objective of this study was to model the left ventricle (LV) based on 2D echocardiography imaging technique to assess the cardiac mechanics for group of patients affected by heart failure. A prospective study has been made at Ibn Al-Bitar center for cardiac surgery, for 13 patients with heart failure (HF), 9 patients were males (69%) and 4 females (31%). The mean age was 54±7 years. Those patients were supposed to undergo a CRT-D (Cardiac Resynchronization Therapy Defibrillator) implant as they didn’t respond to drug therapy. Before CRT-D implantation, 2D echocardiography was performed for all the patients, to model the left ventricle and to measure indices that were used to evaluate cardiac mechanics which are LV pressure, wall stresses, global longitudinal strain, and cardiac output. After 3-months of follow-up, 2D echocardiography was re-assessed and the left ventricular mechanics has been re-measured. Post CRT-D implantation, significant improvement in the cardiac mechanics was observed in 54% of the patients which were called responders (patients that respond to CRT-D device) and the other patients were called non-responders. It has been seen that, the circumferential wall stresses were decreased in responder’s group while increased or remain unchanged in non-responders. Global longitudinal strain for the responder’s group were increased while remain unchanged in the non-responders. So, patients were divided into responders and non-responders, based on improvement of the cardiac mechanics after 3-moths of follow up. It has been concluded that the modelling of the left ventricle based on images obtained from 2D echocardiography imaging techniques, was an important computational tool that was used to enhance understanding and support the evaluation, surgical guidance and treatment management of basic biophysics underlying cardiac mechanics.
This extensive and thorough review aims to systematically outline, clarify, and examine the numerous exploratory data analysis techniques that are employed in the intriguing and rapidly advancing domain of functional MRI research. We will particularly focus on the wide array of software applications that are instrumental in facilitating and improving these complex and often nuanced analyses. Throughout this discourse, we will meticulously assess the various strengths and limitations associated with each analytical tool, offering invaluable insights relevant to their application and overall efficacy across diverse research contexts and environments. Our aim is to create a comprehensive understanding of how these tools can be best utilized to enhance research outcomes. Through this analysis, we aspire to equip researchers with critical knowledge and essential information that could profoundly influence their methodological selections in upcoming studies. By carefully considering these factors, we hope to contribute positively to the ongoing progression of this important field of inquiry, fostering innovation and enhancing the impact of future research findings in functional MRI studies.
Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.
Technically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.
Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.
This research focuses on enhancing the diagnostic power of the slit lamp, a fundamental ophthalmic instrument, by replacing its traditional halogen light source with a cutting-edge white laser. The objective of this modification is to significantly improve the brightness, intensity, and color accuracy, which are crucial for distinguishing fine ocular details during eye examinations. White laser technology offers a more stable, energy-efficient light source with reduced maintenance needs, making it a valuable upgrade over conventional systems. As part of this redesign, the optical system will be optimized with new filters, lenses, and heat management techniques to accommodate the white laser. Additionally, integrating a high-resolution digital camera with the enhanced illumination system is expected to provide sharper, more accurate imaging for better diagnosis. The anticipated outcome is a transformative improvement in ocular diagnostics, leading to earlier and more precise detection of eye conditions. This advancement holds promise for both patients, through better care, and ophthalmologists, through increased diagnostic efficiency. Challenges in implementation and potential solutions are also considered.
The hydrodynamics of stirred tanks and bubble breakup are crucial in gas-liquid flows, yet this system has not been well characterized for different operating conditions. In this work, the numerical method was used to investigate the hydrodynamics of six- flat blades impeller (Rushton turbine) and the results were employed to understand the bubble breakup behavior in the stirred tank. Simulation results of predicted flow pattern, power number, and the distribution of turbulence energy generated were performed with COMSOL Multiphysics. Numerical results showed good agreement with the experimental literature. The effect of rotational speed on bubble breakup behavior, such as breakage probability, the average number of daughter bubbles, and the breakage time was investigated using the high-speed imaging method. The main finding is that the breakage process occurs in the high energy area of high turbulence intensity, which is located within a distance equal to the blade width of a radius of (15-35 mm). The breakage probability (Bp) was found to be increased by 12.61 percent for a mother bubble of 4 mm at 340 rpm, with an average fragmentation of up to 22 fragments. Furthermore, the bubble breakage time was found to decrease with increasing impeller rotational speed, with an average value of 19.8 ms.