Medical Image Processing Algorithms Research Articles

Blood cell image segmentation and classification: a systematic review.

Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

Automated chronic wounds medical assessment and tracking framework based on deep learning

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

Digital x-ray medical image enhancement method based on human visual characteristics

Digital x-ray medical image with human visual characteristics is a kind of medical image formed by x-ray irradiation. Currently, medical image processing algorithm is the main hot topic. With the popularization of medical images in clinical diagnosis and the development of computer technology, the accuracy of clinical diagnosis can be greatly improved using medical image processing technology. For digital x-ray imaging in medical system, it is inevitable that the quality of medical image will be degraded by various factors, so it needs to be processed before they can be used. At the same time, with the development of medical digital x-ray detector technology, the range of medical images used will be much smaller than that of detectors. The medical digital x-ray image data received after detection also need to be processed by an effective image-processing algorithm before display. On the other hand, the research, analysis, and diagnostic use of computer in medical images can appropriately reduce the work intensity of doctors or medical staff, and also play an important role in the discovery of certain diseases. A method of digital x-ray medical image enhancement based on human visual characteristics is studied by analyzing the characteristics of histogram of x-ray image and the maximum entropy fuzzy threshold image enhancement algorithm. The experimental results show that the method can fully recognize the medical x-ray images with incorrect exposure, enhancing the effect of digital x-ray imaging and improving the recognition rate.

Recent advances and clinical applications of deep learning in medical image analysis.

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss major technical challenges and suggest possible solutions in the future research efforts.

Deep learning-based 3D MRI contrast-enhanced synthesis from a 2D noncontrast T2Flair sequence.

Gadolinium-based contrast agents (GBCAs) have been successfully applied in magnetic resonance (MR) imaging to facilitate better lesion visualization. However, gadolinium deposition in the human brain raised widespread concerns recently. On the other hand, although high-resolution three-dimensional (3D) MR images are more desired for most existing medical image processing algorithms, their long scan duration and high acquiring costs make 2D MR images still much more common clinically. Therefore, developing alternative solutions for 3D contrast-enhanced MR image synthesis to replace GBCAs injection becomes an urgent requirement. This study proposed a deep learning framework that produces 3D isotropic full-contrast T2Flair images from 2D anisotropic noncontrast T2Flair image stacks. The super-resolution (SR) and contrast-enhanced (CE) synthesis tasks are completed in sequence by using an identical generative adversarial network (GAN) with the same techniques. To solve the problem that intramodality datasets from different scanners have specific combinations of orientations, contrasts, and resolutions, we conducted a region-based data augmentation technique on the fly during training to simulate various imaging protocols in the clinic. We further improved our network by introducing atrous spatial pyramid pooling, enhanced residual blocks, and deep supervision for better quantitative and qualitative results. Our proposed method achieved superior CE-synthesized performance in quantitative metrics and perceptual evaluation. In detail, the PSNR, structural-similarity-index, and AUC are 32.25dB, 0.932, and 0.991 in the whole brain and 24.93dB, 0.851, and 0.929 in tumor regions. The radiologists' evaluations confirmed that our proposed method has high confidence in the diagnosis. Analysis of the generalization ability showed that benefiting from the proposed data augmentation technique, our network can be applied to "unseen" datasets with slight drops in quantitative and qualitative results. Our work demonstrates the clinical potential of synthesizing diagnostic 3D isotropic CE brain MR images from a single 2D anisotropic noncontrast sequence.

Osteolysis: A Literature Review of Basic Science and Potential Computer-Based Image Processing Detection Methods.

Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. This biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. The eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysis is clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. These deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis.

Medical Images Segmentation Based on Unsupervised Algorithms: A Review

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

The Philosophy of Expertise in the Age of Medical Informatics: How Healthcare Technology is Transforming Our Understanding of Expertise and Expert Knowledge?

Abstract The unprecedented development of medical informatics is constantly transforming the concept of expertise in medical sciences in a way that has far-reaching consequences for both the theory of knowledge and the philosophy of informatics. Deep medicine is based on the assumption that medical diagnosis should take into account the wide array of possible health factors involved in the diagnostic process, such as not only genome analysis alone, but also the metabolome (analysis of all body metabolites important for e.g. drug-drug interactions), microbiome (i.e. analysis of all bodily microorganisms interacting with host cells) or exposome (analysis of all environmental factors triggering potentially harmful cell mutations, such as UV radiation, heavy metals, various carcinogens, etc.). Deep data analysis is of tantamount importance for personalized diagnosis but, at the same time, hardly achievable by a regular human being. However, adequately designed artificial intelligence (e.g. a deep neural network) can undeniably be of great help for finding correlations between symptoms and underlying diseases. Nowadays AI applies to nearly every aspect of medicine, starting from the data analysis of scientific literature, through the diagnostic process, to the act of communication between physicians and their patients. Medical image processing algorithms greatly enhance the chances of successful recognition of melanoma or intestinal polyps. Communication tools designed for physicians use techniques known from social media to provide users with an opportunity to consult the case with colleagues from the same discipline. Natural language processing tools significantly improve doctor-patient communication by the automation of note-taking. Is this enough to support the claim that the non-epistemic competences in medicine are becoming more and more important? Can we attribute expertise only to a person? How is medical informatics changing the way most people usually understand human-computer interactions?

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance Than Deep Neural Networks.

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

Deep learning algorithms for medical image processing

Deep learning algorithms for medical image processing

Advanced Algorithms for Medical Image Processing

Advanced Algorithms for Medical Image Processing

Chest radiograph image enhancement with wavelet decomposition and morphological operations

Medical image processing algorithms significantly affect the precision of disease diagnostic process. This makes it crucial to improve the quality of a medical image with the goal to enhance perceivability of the points of interest in order to obtain accurate diagnosis of a patient. Despite the reliance of various medical diagnostics on utilize X-rays, they are usually plagued by dark and low contrast properties. Sought-after details in X-rays can only be accessed by means of digital image processing techniques, despite the fact that these techniques are far from being perfect. In this paper, we implement a wavelet decomposition and reconstruction technique to enhance radiograph properties, some of which include contrast and noise, by using a series of morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of cancer nodules.

Performance of Medical Image Processing Algorithms Implemented in CUDA running on GPU based Machine

Performance of Medical Image Processing Algorithms Implemented in CUDA running on GPU based Machine

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Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.

Extended PCJO for the Detection-Localization of Hypersignals and Hyposignals in CT Images

The anthropomorphic model observer (MO) plays an important role in the assessment and optimization of medical imaging systems. The MO is a task-based approach; while the abnormality can appear as a hypersignal or a hyposignal for different imaging modalities, sequences, or organs, no MO has been proposed for the hyposignals detection-localization task in the literature. To improve the clinical relevance of the existing MOs, we propose an anthropomorphic MO that can also deal with hyposignals in this paper. In a previous study, we reported a perceptually relevant channelized joint observer (PCJO) for detecting and localizing multiple signals with unknown amplitude, orientation, size, and location. Here, we extend it mathematically to hyposignals task. A free-response study (close to the real-diagnostic procedure) for both hypersignals and hyposignals in cerebral and abdominal CT images was conducted with four radiologists. The equally weighted alternative free-response operating characteristic was used as the figure of merit. Statistical analyses show that the extended PCJO approaches the experts’ performances with no significant difference in the studied tasks. The results demonstrate that the extended PCJO is an alternative to replace radiologists for the evaluation and comparison of different medical image processing algorithms. The PCJO has been originally proposed on magnetic resonance imaging but tested on computerized tomography (CT) here; the coherent results show that the PCJO can be generalized to another modality-CT. We also provide in this paper, the reference values of all the parameters in the PCJO to facilitate its future application on magnetic resonance (MR) or CT images.

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.

Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction.

Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T2-w whole head (including brain, skull, eyes etc) images from T1-w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T2-w image. We show that our synthetic T2-w images can be used as a template in absence of a real T2-w image. Our patch based method requires multiple atlases with T1 and T2 to be registeLowRes to a given target T1. Then for every patch on the target, multiple similar looking matching patches are found on the atlas T1 images and corresponding patches on the atlas T2 images are combined to generate a synthetic T2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.

Implementation on FPGA Circuit of the Medical Image Processing Algorithms within the Framework of Assistance to the Medical Diagnosis

The main interest of this paper is the classification Algorithms of a Magnetic Resonance Imaging (MRI) of the human brain developed in a Very High speed integrated circuit of Description Language (VHDL) in order to implement it on Field Programmable Gate Array circuit (FPGA) within the framework of assistance to the diagnosis. Neuronal networks of a Learning Vector Quantization (LVQ) type were used. The training of LVQ is done in MATLAB in order to exploit synoptic weights of the neurons as a database for classification algorithm which will be implemented on cyclone II FPGA circuit of Altera development card (DE2). The purpose of this work is to achieve a real time hardware implementation with higher performance in both size and speed.

SU-E-I-59: Virtual Phantom: A First Step to a Comprehensive Automated Quality Control Program for Magnetic Resonance Image Guided Applications

Purpose: To design a virtual phantom for the calculation of quality metrics required for controlling MRI guided applications and to develop software tools to automatically perform the required controls. Methods: The virtual phantom is designed using 3D objects arranged in different dispositions in the space. Six cylinders of 6mm diameter and 500mm length, tilted 30 degrees with respect to the Y axis, are used for slice thickness and location. These cylinders are placed in coronal planes distanced by 60mm. Their width and position allow measuring the slice location and thickness. A 15*15*15 mm3 cube centered at the origin and rotated 3 degrees with respect to the Z axis is used for the calculation of the vertical and horizontal Modulation Transfer Function. Spheres of 10 mm diameter distanced by 20 mm covering a field of view of 500mm are used for estimating the in-plane and through-plane geometric distortion. Sets of cylinders with lengths ranging from 0.5mm to 2.0mm and diameters ranging from 4mm to 10mm are used for the low contrast. The Low Contrast test assesses the number of cylinders that can be detected.In-house Java based software is developed and validated. The software automatically corrects positioning errors before constructing ROIs and acquiring measurements with respect to each control. Results: The virtual phantom was used to produce a large set of 2D DICOM images. Results showed that the software robustly calculated positions and distances with sub-voxel accuracy. For a 500mm FOV and a 0.9mm pixel size, mean errors were in the order of 0.15mm. Conclusion: This virtual phantom is an important step for building a new physical phantom and for validating new medical image processing algorithms. These tools represent a first step toward designing a comprehensive phantom and software for a complete Quality Control program for MRI guided application.

Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop

Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop