Medical Image Processing System Research Articles

VESCL: an open source 2D vessel contouring library.

VESCL (pronounced 'vessel') is a novel vessel contouring library for computer-assisted 2D vessel contouring and segmentation. VESCL facilitates manual vessel segmentation in 2D medical images to generate gold-standard datasets for training, testing, and validating automatic vessel segmentation. VESCL is an open-source C++ library designed for easy integration into medical image processing systems. VESCL provides an intuitive interface for drawing variable-width parametric curves along vessels in 2D images. It includes highly optimized localized filtering to automatically fit drawn curves to the nearest vessel centerline and automatically determine the varying vessel width along each curve. To support a variety of segmentation paradigms, VESCL can export multiple segmentation representations including binary segmentations, occupancy maps, and distance fields. VESCL provides sub-pixel resolution for vessel centerlines and vessel widths. It is optimized to segment small vessels with single- or sub-pixel widths that are visible to the human eye but hard to segment automatically via conventional filters. When tested on neurovascular digital subtraction angiography (DSA), VESCL's intuitive hand-drawn input with automatic curve fitting increased the speed of fully manual segmentation by 22× over conventional methods and by 3× over the best publicly available computer-assisted manual segmentation method. Accuracy was shown to be within the range of inter-operator variability of gold standard manually segmented data from a publicly available dataset of neurovascular DSA images as measured using Dice scores. Preliminary tests showed similar improvements for segmenting DSA of coronary arteries and RGB images of retinal arteries. VESCL is an open-source C++ library for contouring vessels in 2D images which can be used to reduce the tedious, labor-intensive process of manually generating gold-standard segmentations for training, testing, and comparing automatic segmentation methods.

Deep Learning Approaches for Medical Image Processing in the Big Data Era

Diagnostics, therapy planning, and patient monitoring are areas where medical image processing has become indispensable in modern healthcare. The advent of deep learning techniques and the broad usage of big data in healthcare have brought a revolutionary shift in medical picture analysis and interpretation. An overview of deep learning techniques for medical image processing based on big data is given in this article. This article covers the pros and cons of adopting big data for medical imaging, from data storage and analysis to data capture. Beyond that, we take a look at medical image analysis using deep learning algorithms such as recurrent neural network (RNN), Convolutional Neural Network (CNN), and generative adversarial network (GAN), and we highlight its advantages and disadvantages. We also examine recent innovations such as transfer learning, multi-modal imaging fusion, and federated learning, which can improve the accuracy and efficiency of medical image processing systems. Finally, we discuss how medical image processing driven by deep learning could improve clinical decision-making, patient outcomes, and the development of personalized medicine in the era of data-driven healthcare.

An intelligent deep network for dental medical image processing system

Nowadays, many people are affected by oral health issues because of continuous changes in lifestyle such as personal speech that is affected by crooked teeth and malocclusion teeth. Moreover, a dental problem can cause bacterial infections, cavities, and many other diseases due to an improper lifestyle. In this research, a novel Intelligent Ant Lion-based Convolution Neural Model (IALCNM) is designed for segmenting affected parts in teeth and to classify the wear and periodontitis diseases from the collected dataset. Moreover, the developed technique is implemented in the python 3.8 environment and the attained results of the developed procedure are related to other existing techniques implemented for different diseases in standings of accuracy, precision, error rate, execution time, and so on. Hence the outcome indicates that the current research technique applied to self-created datasets has enhanced the accuracy of segmenting affected parts and disease prediction more than other techniques.

Identifying Obviously Artificial Medical Images Produced by a Generative Adversarial Network.

Synthetic medical images have an important role to play in developing data-driven medical image processing systems. Using a relatively small amount of patient data to train generative models that can produce an abundance of additional samples could bridge the gap towards big-data in niche medical domains. These generative models are evaluated in terms of the synthetic data they generate using the Visual Turing Test (VTT), Fréchet Inception Distance (FID), and other metrics. However, these are generally interpreted at the group level, and do not measure the artificiality of individual synthetic images. The present study attempts to address the challenge of automatically identifying artificial images that are obviously-artificial-looking, which may be necessary for filtering out poorly constructed synthetic images that might otherwise deteriorate the performance of assimilating systems. Synthetic computed tomography (CT) images from a progressively-grown generative adversarial network (PGGAN) were evaluated with a VTT and their image embeddings were analyzed for correlation with artificiality. Images categorized as obviously-artificial (≥0. 7 probability of being rated as fake) were classified using a battery of algorithms. The top-performing classifier, a support vector machine, exhibited accuracy of 75.5%, sensitivity of 0.743, and specificity of 0.769. This is an encouraging result that suggests a potential approach for validating synthetic medical image datasets. Clinical Relevance - Next-generation medical AI systems for image processing will utilize synthetic images produced by generative models. This paper presents an approach towards verifying artificial image legibility for quality-control before being deployed for these purposes.

Generation of Random Fields for Image Segmentation Techniques: A Review

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs.

Comparison Between Median Filter and Wiener Filter to Get High Accuracy for Blood Vessel Image Extraction

With today's advancing technology, support of developing hardware and software systems, the developments in the field of medicine have increased considerably. In particular, medical image analysis and processing systems have taken a considerable lead. The development of an automatic system could provide great convenience for doctors and practitioners in the field. The image processing techniques proposed in this study can contribute to more effective analysis and more accurate diagnosis, regardless of the individual levels of experience of the users or particular situations and conditions such as fatigue or image quality. This paper presents a robust method for retinal blood vessel segmentation and some automatic algorithms for analyzing the vessel network and pixel classification into vessel and non-vessel .The aim of the work extraction or segmentation of retinal blood vessels used computer vision and image processing for getting high accuracy (comparison with manual) .Also We used the preprocessing techniques for enhancement of the image .And used two types of filter for comparing result to get best scenario. Also for simulation result we used the matlab and implement on DRIVE database.

A Customizable Thermographic Imaging System for Medical Image Acquisition and Processing

A custom system has been developed for medical image acquisition and processing in both the visible and the infrared (IR) bands. Unlike some non-customizable commercial devices, this system can easily be adapted to different application scenarios by adding new peripherals and/or custom image processing algorithms. Portable, autonomous, and easy to use, it offers immediate results at the moment of examination and has a competitive cost. The system comprises a Single Board Computer (SBC) controlling a group of sensors and peripherals. The hardware implementation, described in detail in this paper, was adapted for two different application scenarios. First, the system was employed to differentiate between different kinds of vascular anomalies. The clinical results obtained are reported. The device was then redesigned to automatically detect people with high body temperatures in public environments. The system’s real-time image processing capabilities in both scenarios are demonstrated. Specific algorithms were implemented by the authors for each case study.

3-D Block-Rooting Scheme With Application to Medical Image Enhancement

The goal of image enhancement is to improve certain features and details on an image. It is a critical procedure in image processing (image segmentation, feature extraction), medical imaging (such as X-ray, CT, and MRI) and computer vision (target, object, and text detection; segmentation, registration, and recognition). X-ray, CT, and MRI images are often affected by blurriness and lack of contrast, but the clarity of these images is very important for the accuracy of medical diagnosis and treatment. This article offers (1) a 3-D transform block-rooting scheme-based image enhancement method and (2) a non-reference transform-domain quality measure to choose the presented algorithm parameters optimally. Experimental results from NYU, fastMRI, and ChestX-ray data sets show that the proposed technique performs well and can reduce noise during the sharpening of the image details. At the same time, it can undoubtedly be used in different medical image processing systems.

A Medical Image Processing System for Visualization and Quantification of a Human Brain

A Medical Image Processing System for Visualization and Quantification of a Human Brain

Medical image processing using python and open cv

This paper presents the implementation of the Python programming language and the Open CV library in medical image processing. Medical images play an important role in identifying diseases and detecting whether organs are functioning properly. Image processing related to medical images is an active research field where various techniques are used to facilitate diagnosis and various image processing techniques can be used. In this study, image processing was performed including image preprocessing, histogram equalization, smoothening, erosion, and dilation. The use of python was chosen because this programming language is open source with a lot of available resources. The open CV library is also available free of charge and provides tons of image processing functions. The software can also be installed in a small Raspberry Pi computer and low electrical power usage making it very suitable as an efficient resource of the medical image processing system.

The use of raspberry pi as a portable medical image processing

This paper presents the image processing implementation in Raspberry Pi. Raspberry Pi is a system built-in and is a low-cost computer that can be used to reduce system complexity in real-time applications. For programming, it’s very good to use Python. Raspberry pi has a Camera Slot Interface (CSI) to interface the camera with Raspberry Pi. Low contrast images captured using the Raspberry Pi camera module can be scaled up first to identify specific image regions. The most important processing is the removal of noise as a result of noise disturbance during image acquisition on the camera. Due to its small size, which is about the size of a credit card and its lightweight, raspberry pi is very suitable for use as a portable medical image processing system. Besides that Raspberry Pi also consumes low power.

Pixel Value Ordering Based Reversible Data Hiding with Novel MPBS Strategy

Recently, Reversible Data Hiding (RDH) techniques has gained much attention in many sensitive fields such as remote sensing, archive management, military and medical image processing systems. This is due to the lossless data extraction ability of RDH schemes. The primary goal of RDH schemes is to achieve high embedding rates while maintaining the quality of cover objects. For achieving better performance, Pixel Value Ordering (PVO) based reversible data hiding schemes have been proposed. PVO refers to the process of ranking the pixels in blocks and then modifying the pixels according to some embedding rules/conditions. So far, the existing PVO techniques have considered neighborhood pixels at unit distance. In this paper, an improved RDH using block based PVO scheme is proposed which exploits the pixel correlation efficiently by increasing the block size and applying a novel Median Pixel based Block Selection Strategy (MPBS). When block size is increased, the ordering of pixels is changed after embedding. So, to extract the secret data in a lossless manner, the secret bits are swapped in accordance with their corresponding Stego pixels’ index order. Also, the overflow and underflow conditions are effectively handled using Location Map. Experimental results show the better performance of the proposed RDH technique with the existing technique.

Special issue on video and imaging systems for critical engineering applications [SI 1096

A recent change in the global economy, experiencing an unparalleled integration of science and technology, i.e., smart watches, smartphones and other electronic gadgets, has dramatically changed the way we connect with the world around us. Various challenges and issues brought by this integration for mobile and video lie ahead, and many research questions remain to be answered. For instance, the complex nature of mobile and video imaging need radical version to the swift changes in our society, economy, environment, and technological revolution; there is an utmost need to address and propose dynamic and innovative solutions to problems which tend to be either complex or static or rapidly evolving with a large number of unknowns. Furthermore, the battle against the severe disease such as cancer, tuberculosis, high blood pressure and ECG, fight against the terror, exploration and management of the natural resources in remote area using remote sensing, underground and tunnel inspection, other and environmental monitoring are some of the areas that need to be addressed with urgency. In addition, the complication of involved imaging scenarios, and demanding design parameters, i.e., signal to noise ratio, high speed specification, high contrast and spatial resolution, complex background, harsh environment, requires the growth of multifunctional, scalable imaging suit of sensors, solutions driven by novelty, operating on diverse detection and imaging principles. Moreover, artificial neural network when combined with pattern recognition techniques, such as classification, feature selection, clustering, text analysis, image and color representation of video and image processing promise the solution of challenging technical problems, below the umbrella of complex video and imaging situations of stimulating technical problems, with applications in medical video and image processing, aerospace, radars and defense system, and other security appliances. Witnessing all these mobile breakthroughs, therefore, we believe that it is important to investigate the current advances and future trends in the mobile image and video processing.

A Fuzzy Multi-Objective Covering-based Security Quantification Model for Mitigating Risk of Web based Medical Image Processing System

Medical image processing is one of the most active research areas and has big impact on the health sector. With the arrival of intelligent processes, web based medical image processing has become simple and errorless. Web based application is now used extensively for medical image processing. Large amount of medical data is generated daily with more and more data being shared over public and private networks for the diagnosis of diseases through the web based image processing systems. Medical images like that of the CT (Computed Tomography) scan, MRI (Magnetic Resonance Imaging), X-Ray and Ultrasound images, etc., contain highly personal data of the patients. This data needs to be secured from intruders. Medical images are more sensitive to external interruption and manipulation in data may cause changes in the result. Data breaches in medical cases can lead to wrong diagnosis or even more fatal possibilities with life threatening results. So, security in web based medical image processing is a major issue. However, ensuring security for the medical images while preserving the characteristics of confidentiality, integrity, availability, etc., of medical images poses a major challenge. Working towards a feasible solution, in this study, authors are using a list of criteria for checking security level of the web based image processing system. We propose Fuzzy Analytic Hierarchy Process (FAHP) combined with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in the list of criteria that affect the security assessment in medical image processing. At the results we see that FAHP-TOPSIS produce good results in security checking in web based medical image processing system. At the data analysis section all the steps showed which is involved in our model.

Digital anatomic study of the ureter relative to bifurcation of the common iliac artery in females

Introduction Laparoscopic salpingo-oophorectomy and pelvic lymphadenectomy place the ureter at risk. Although traditional anatomic studies indicate that ureters generally cross over the common iliac artery in the pelvic inlet, the view is often different during surgery. Hence, digital three-dimensional models have been utilized to solve this conundrum. Our study aimed to evaluate the anatomic relation between the iliac artery and the ureter in the pelvic inlet. Material and methods A medical image processing system processed data collected from 129 female patients, and digital 3D models of the iliac artery and ureter were used to study their anatomic relation. Left- and right-side data were compared using Pearson’s χ2 analysis. Results Two different anatomic relations were found immediately posterior to the ureter: Common iliac arteries were present in 33.2% and external iliac arteries in 76.8%. The mean distances from the point at which the ureter crossed over the iliac artery until the bifurcation of the artery was 8.8 ± 12.2 mm in the left pelvis and 12.4 ± 10.8 mm in the right pelvis – different from that of the traditional anatomic study. Conclusion In the future, these models and digital anatomical data could help avoid surgical complications.

Convolutional Neural Network Based Medical Image Classifier

Deep learning had provided good outcome in analyzing images of tumours, however, the deficiency of large annotated datasets reduces its importance. The proposed medical image processing system is based on image segmentation and image classification. It is to be used by medical field experts. In order to classify the Brain tumour images the semantic level classification and segmentation network techniques are applied. This includes prior knowledge of testing samples and training samples, using Convolutional Neural Networks (CNN). The CNN based classifier improves the detection accuracy compared to the existing segmentation based classifier. In this project, the automated system would help the medical image analyst to identify the Brain Tumour in patient by making use of deep convolutional neural network (CNN).The image is obtained from MRI scan of a brain. The tumourless patient’s image dataset is used as the training and testing data for the classification network. Patient image is compared with dataset of a tumour affected images for differentiating an image as Non-timorous sample, low grade glioma and glioblastoma after segmenting and classifying the image. The Watershed segmentation algorithm is used for segmenting images and CNN is used for classifying the images. Finally the system will detect the tumour is affected or not in the given image of a patient’s brain, then the system will identify the tumour affected region and differentiate the low grade glioma and glioblastoma in the image.

RETRACTED ARTICLE: A novel registration and super-resolution jointed paradigm for medical images under internet of thing environment

This paper proposes a novel registration and super-resolution jointed paradigm for medical images under the Internet of thing environment. In the medical image processing, the matching issue is one catches wide attention with the domain of research. Image registration technique can be divided into similarity measure, optimization, geometric transformation, and interpolation, etc. As the first essential clue of our model, we propose the novel registration algorithm based on energy feature extraction. Generally, the matching energy function by the similarity measurement and a penalty constitution is called the external force and endogenic force separately. The matching is an external force and endogenic force mutual competition, eventually achieves the balanced process. Furtherly, we integrate the game analysis and area feature selection to achieve the better image super-resolution mode through the pretreatment of the image to change the initial value, so as to achieve the purpose of improving the performance. Besides the algorithm level innovation, we integrate the GPU and the IOT to construct the hardware based implementation of the proposed medical image processing system. The latency of registers to read and write data across a GPU’s entire storage system is minimal, it is private to each thread, and can only be accessed by its owning thread. For each thread, the local memory is also private and it is often used to deal with the problem of overflow register, reducing the buffer overflow caused by the entire application of a substantial decline in the possibility and shared memory is visible to all threads within the thread block. We then achieve the optimal integration of IOT and GPU. The experimental result proves the robustness of the method.

RayPlus: a Web-Based Platform for Medical Image Processing.

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

A digital anatomical study of the corona mortis in females

It was a different study from traditional anatomy to construct three-dimensional (3D) model of the corona mortis (CMOR) through contrasted enhanced computed tomography (CT) scanning. This will provide an aid for the understanding of the fine anatomy of CMOR, as well as surgical produces. A medical image processing system was used to process computed tomography scanning data collected from 330 cases of woman patients. Accurate digital 3D models of the CMOR were used to study the incidence, location, and the morphological patterns. These models have a rotation of 360° and allow displaying from different views. The incidence was 51.1% for the venous CMOR, 14.1% for the arterial CMOR in the 660 sides pelvic wall and vessel models. The mean distance from the symphysis pubis for the venous CMOR was 66.87 mm, and 59.60 mm for the arterial CMOR. The morphological patterns we identified were classified in three types (I-III). The model of CMOR serves as a useful tool for the learning and investigation of the CMOR. In the future, the digital anatomical data can be beneficial to illustrating the operation approach and decreasing the surgical complications.

Anatomical understanding in canal wall down mastoidectomy using a medical image processing system – simulation and education of ear surgery

Anatomical understanding in canal wall down mastoidectomy using a medical image processing system – simulation and education of ear surgery