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International Journal of Biomedical Imaging (IJBI)

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Factor de Impacto:
1.3
Editor:
Hindawi
ISSN:
1687-4188
Vistas:
23320
Seguidores:
3

Solicitud de Artículos

International Journal of Biomedical Imaging (IJBI) is an academic journal published by Hindawi. (ISSN 1687-4188, impact factor 1.3).

Aims and scope The last quarter century has witnessed major advancements that have brought biomedical imaging to a paramount status in life sciences. As a prominent example, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) was established in 2000 as the newest institute in the National Institutes of Health (NIH), USA. Generally speaking, the scope of biomedical imaging covers data acquisition, image reconstruction, and image analysis, involving theories, methods, systems, and applications. While tomographic and post-processing techniques become increasingly sophisticated, traditional and emerging modalities play more and more critical roles in anatomical, functional, cellular, and molecular imaging. The overall goal of the International Journal of Biomedical Imaging is to promote research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. International Journal of Biomedical Imaging is operated by a board of editors consisting of internationally known active researchers. The journal is made available online for free and can also be purchased in print. It utilizes a web-based review process for speedy turnaround up to high standards. In addition to regular issues, special issues will be organized by guest editors. Subject areas include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
Última actualización por Dou Sun el

Special Issues

Special Issue on Machine Learning Approaches in Brain Tumor Segmentation Día de Entrega: 2026-08-31 Description Biomedical imaging has emerged as an indispensable pillar of modern healthcare, enabling non-invasive visualization, diagnosis, and monitoring of a wide range of medical conditions. With the rapid evolution of imaging modalities such as MRI, CT, PET, ultrasound, and optical imaging, there is an unprecedented ability to capture high-resolution anatomical, functional, and molecular information. These advances, coupled with the integration of artificial intelligence (AI), machine learning (ML), and computational modeling, are revolutionizing the way clinicians interpret images, detect abnormalities, and tailor patient-specific treatment plans. Brain tumour segmentation plays a vital role in accurate diagnosis, treatment planning, and prognosis in neuro-oncology. With the rapid advancement of artificial intelligence, machine learning and deep learning techniques have emerged as powerful tools for automating the segmentation process from medical imaging data, particularly Magnetic Resonance Imaging (MRI). Despite these advancements, several challenges continue to hinder the full realization of biomedical imaging’s potential. High data complexity, variability in acquisition protocols, and the presence of noise and artifacts often limit the reproducibility and accuracy of image interpretation. Moreover, the integration of heterogeneous imaging data from multiple modalities requires sophisticated algorithms capable of handling large-scale, multidimensional datasets while ensuring interpretability and clinical relevance. Regulatory, ethical, and data privacy concerns also present barriers to the widespread adoption of AI-driven imaging solutions in clinical practice. Additionally, real-time processing demands in critical care, intraoperative guidance, and point-of-care diagnostics remain largely unmet, necessitating the development of more efficient and robust computational approaches. This Special Issue aims to bring together advanced research and innovative methodologies that address the challenges and opportunities in brain tumour segmentation using machine learning. We welcome contributions that explore novel algorithms, deep learning architectures, image preprocessing strategies, and feature extraction techniques for accurate and efficient segmentation of brain tumours from medical imaging modalities, particularly Magnetic Resonance Imaging (MRI). Topics of interest include AI-driven diagnostic frameworks, including convolutional neural networks (CNNs) and hybrid deep learning architectures, multimodal imaging fusion, automated tumour classification, and the integration of quantitative imaging biomarkers for clinical decision support. Special emphasis will be placed on approaches that enhance segmentation accuracy, improve robustness across diverse datasets, reduce computational complexity, and enable real-time analysis in clinical environments. By fostering collaboration between computer scientists, radiologists, and biomedical engineers, this Special Issue seeks to bridge the gap between algorithmic innovation and clinical applicability, ultimately contributing to early detection, precise treatment planning, and improved outcomes for patients with brain tumours. Potential topics include but are not limited to the following: Advanced image (particularly MRI) reconstruction and enhancement techniques CNN and deep learning architectures for precise delineation of tumor regions such as gliomas, meningiomas, and pituitary tumors Integrating automated brain tumor segmentation into clinical workflows Computational models for precision diagnostics and therapeutics
Última actualización por Dou Sun el

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