The primary objective of this study is to provide an extensive review of deep learning techniques for medical image recognition, highlighting their potential for improving diagnostic accuracy and efficiency. We systematically organize the paper by fi...
Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functiona...
Through the lunate bone biomechanical analysis can be used for kienböck medical diagnosis and treatment. The pressure sensitive film can reflect intuitively the stress of lunate bone's each point. This paper obtains stress analysis of lunate bone bas...
Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis,...
Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to...
The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate...
Over the last decade, Medical Imaging has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of vi...
Breast cancer is the most common malignancy among women and a leading cause of cancer-related mortality, making early and accurate detection essential. This review summarises advances in breast imaging and computational diagnostics across mammography...
The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To impr...
The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN arch...
Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL) [...]
This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) t...
Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To...
Whole-slide images and computed tomography scans are commonly decomposed into patches or slices, whereas diagnostic labels are assigned only at the study level, so that which patches or slices support the diagnosis remains unknown. Multiple-instance...
Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized...
Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this...
Medical imaging (MI) [...]
Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees f...
Artificial intelligence methods have evolved sufficiently to be widely applied in automatic data analysis, providing standardized and reproducible results comparable to those of highly skilled specialists, while assisting less experienced personnel [...
Colorimetric sensing techniques for point(s), linear and areal array(s) were developed using image sensors and novel image processing software for chemical, biological and medical applications. Monitoring and recording of colorimetric information on...
Electron microscopy is fundamental to modern materials characterization; however, the quantitative analysis of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) data remains challenged by noise, imaging artifacts, acquisit...
Medical Imaging is becoming an essential component in various fields of bio-medical research and clinical practice: Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imagin...
Texture features can capture microstructural patterns and tissue heterogeneity, playing a pivotal role in medical image analysis. Compared to deep learning-based features, texture features offer superior interpretability in clinical applications. How...
We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and se...
Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in autom...
Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG tas...
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospe...
Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D...
The medical field is experiencing remarkable advancements, notably with the latest technologies—artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)—that are in place to offer groun...
22 June 2021
At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To...
Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial p...
Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimiz...
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone...
Background/Objectives: Timely and accurate medical imaging in pre-hospital emergency care is crucial for improving the effectiveness of emergency medical rescue. Methods: In this study, a portable endoscopic system was developed for image acquisition...
This study introduces a new approach to image enhancement by integrating ideas from geometric function theory with modern computer vision techniques. A specific subclass
Artificial intelligence (AI) is currently becoming a leading field in data processing [...]
Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer dete...
This paper introduces a new method for real-time high-density impulsive noise elimination applied to medical images. A double process aimed at the enhancement of local data composed of Nested Filtering followed by a Morphological Operation (NFMO) is...
Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determ...
Background/Objectives: This paper presents a medical image analysis application designed to facilitate advanced edge detection and fuzzy processing techniques within an intuitive, modular graphical user interface. Methods: Key functionalities include...
Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these...
Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with e...
Magnetic resonance imaging (MRI) is a standard procedure in medical imaging, on a par with echography and tomodensitometry. In contrast to radiological procedures, no harmful radiation is produced. The constant development of magnetic resonance imagi...
7 September 2024
Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images...
Multilevel thresholding partitions medical images into intensity regions. However, increasing threshold counts challenges the exploration–exploitation balance of metaheuristic algorithms, necessitating efficient solutions for clinical pre-proce...
A patient’s imaging record is fragmented across modalities and viewers. CT and MRI arrive as DICOM volumes, whereas pathology is captured as gigapixel whole-slide images (WSI), each in its own coordinate system and scale. This paper presents a...
In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increase...
Quantum image processing provides significant storage benefits over classical methods. However, current quantum image representation techniques exhibit limitations regarding encoding efficiency, circuit complexity, and adaptability to image content....
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding pro...
Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, ima...
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