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Go to Editorial ManagerMesopotamian cities were formed sometime during the fourth millennium BCE, and many of them continued to be inhabited as much as 3000 years. While urban characteristics of these cities has been extensively studied, the current article is concerned with exploring the inhabitants' daily experience in the city; a subject that has not been sufficiently explored despite its importance in urban studies. The objective is to expand the understanding of the relation between the ancient city and its occupants. The paper adopts the concept of the City Image as introduced in the seminal work of Kevin Lunch "Image of The City" in investigating aspects of the Mesopotamian city that qualifies it to form a strong mental Image for her citizens, derived from the legibility of its elements and the structure they form. Using a descriptive analytical method in reviewing previous literature, the research first clarifies the shared characters of Mesopotamian cities, and addresses the stature of the city in Mesopotamians' culture. I then specify the five urban elements of the city image as categorised by Lynch; paths, nodes, edges, districts and landmarks, in addition to addressing manifestations of the citizens' urban life in the Mesopotamian city. Afterward, visualization of the citizen's daily experience through the urban fabric of the city is provided, to arrive at a conclusion of the Legibility of the mental image of the Mesopotamian city in the perception of its citizens.
The security level and robustness of memristive image encryption techniques depend on the order and dynamics complexity of the memristive system. The grid multi-double-scroll (GMDS) chaotic system (CS) offers extremely rich dynamics but the implementation of high-order chaos needs large computation time. To overcome this limitation, researchers have proposed the use of muti-lower-order CSs to assist the encryption process individually. This scenario may reduce the security level since the non-friendly user may attack each involved CS independently. This paper proposes an effective six-dimensional (6D) memristive chaotic system constructed by combining 5D, 5D, and 7D GMDS chaotic systems. Each of the six chaotic sequences is generated from three sequences corresponding to two or three of the basic CSs. The combined CS shares the same total key parameters (initial values and design parameters associated with the three basic CSs) and this leads to a key space of 22392, the highest among the reported image encryption techniques. The combined CS is used to assist the operation of a proposed color image encryption scheme consisting of four sequential stages that perform compressive sensing, scrambling, DNA encoding, and diffusion, respectively. Simulation results validate the feasibility and robust security of the proposed encryption scheme.
A proposed modern technique for determination the blood group typing by monitoring the agglutination of red blood cells using acousto-optical technique and digital camera. The method based on analysis the digital image of the agglutination process by MATLAB software._x000D_ We present an overview of two acousto-optic sensing approaches; the first demonstrates the cuvette approach while the second is the microscope slide approach. The cuvette approach digital image analyzing depends on the green channel distribution of the original image and count the brighten pixels, while the microscope slide approach passes through series of algorithms started with grayscale filter and end with edge detection it counts the different color pixels._x000D_ The experimental result shown that it is possible to enhance the determination of blood group typing by using acousto-optical technique in both cases of using isohemagglutinating sera as well as the crossmatch test in a short time and high efficiency compared with the traditional methods.
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.
As a result of the tremendous development taking place in modern systems and technologies in the field of electronic monitoring. Intelligent monitoring, decision making, and automated response systems have become common subjects at this time, especially after the development of machines responsible for these processes. Traffic surveillance is a trend goal nowadays using different techniques and equipment. In this article, real-time Object detection and tracking techniques were proposed for traffic surveillance using image processing techniques. A state was specifically examined for its ability to detect and count passing motorcycles on a highway in a specific area. The results showed good reliability, with a frame processing time of approximately about (30 ms) and the achievement of real-time performance. The main contribution of this article is reaching the best result implemented by the performance the real-time process using image process technique and tracking the object by depending on the sequencing of frames and can stands with rationally not so powerful machines. Several tools have been used for different types of necessary tasks that will be part of the required application such as Python 3.7; which was used to build the basic algorithms,Visual studio code (VSC) as an Integrated Development Environment (IDE), and Anaconda navigator for downloading many useful libraries. The specifications of the used device were Intel(R) Core (TM) i7- 10750H CPU @ 2.60GHz 2.59 GHz, RAM 16.0 GB, NVIDIA GeForce GTX 1650 GPU, 64-bit operating system, x64-based processor.
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.
One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.
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.
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.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
Cone-beam computed tomography (CBCT) is an indispensable method that reconstructs three dimensional (3D) images. CBCT employs a mathematical technique of reconstruction, which reveals the anatomy of the patient’s body through the measurements of projections. The mathematical techniques employed in the reconstruction process are classified as; analytical, and iterative. The iterative reconstruction 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. The aim of this research is to accelerate the iterative methods by performing the reconstruction process using a graphical processing unit (GPU). This method is tested on two iterative-reconstruction algorithms (IR), the algebraic reconstruction technique (ART), and the multiplicative algebraic reconstruction technique (MART). The results are compared against the traditional ART, and MART. A 3D test head phantom image is used in this research to demonstrate results of the proposed method on the reconstruction algorithms. The simulation results are executed using MATLAB (version R2018b) programming language and computer system with the following specifications: CPU core i7 (2.40 GHz) for the processing, with a NIVDIA GEFORCE GPU. Experimental results indicate, that this method reduces the reconstruction time for the iterative algorithms.
Two lasers were utilized for silicon processing using photoelectrochemical etching and laser texturing in order to produce nano/micro structures, respectively. Photoelectrochemical etching process utilizes a CW diode laser of 532 nm wavelength was used to support electrochemical etching for both n-type and p-type conductivity. While laser texturing process was employed using pulsed fiber laser of 1064 nm wavelength. Various characterization methods were devoted to examine silicon micro/nanostructures surfaces produced by lasers. These methods include AFM, SEM and Raman scattering to provide clear evidence about formation of micro/nanostructures abundant at silicon surfaces. Moreover, FTIR analysis for the laser produced silicon surfaces could emphasize whether the resultant silicon surface is hydrophilic or hydrophobic. Image analysis software adopted a side view micro image was used to measure the contact angle between the water droplet and silicon micro/nano-surfaces. It is found that the laser produced silicon nanostructure by photoelectrochemical etching creates a hydrophobic surface and even super hydrophobic with contact angle of 130 degrees for 50 nm average size. In addition, utilizing fiber laser of high repetition rate for laser texturing produces microstructures that are super hydrophilic with contact angle could reach 8 degrees for a surface dimension of 50 μm.
Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.
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.
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%.
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.
In the field of engineering, 3D printers are indispensable due to their high precision. This study focuses on the construction and optimization of a 3D printer using aluminum T-slotted bars for the frame, Raspberry Pi 4 for control, and Lightburn software for image printing and machine control. After assembling the main components and programming with Marlin firmware, the machine was tested for vibration and noise reduction. The research compared the vibration of a diode laser and spindle during printing, revealing significantly lower vibration with the laser compared to the spindle. These findings demonstrate the effectiveness of the constructed 3D printer in reducing vibration and noise during operation.
Facial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines.Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step. Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.
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.
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.