Computer Vision Algorithms for Biomedical Image Processing

Dear Colleagues,

Computer vision is a branch of artificial intelligence and computer science that enables computers to acquire a high level of comprehension from digital images or recordings, analogous to how humans perceive and interpret visual information. It involves the development of algorithms and techniques that enable machines to analyze visual data and derive meaningful information from it. Computer vision algorithms for biomedical image processing typically explore and present the latest advancements, research, and applications of computer vision techniques explicitly tailored to analyzing biomedical images.

This Special Issue aims to bring together researchers and practitioners from the fields of computer vision and biomedical imaging to showcase cutting-edge developments in computer vision algorithms and their applications in biomedical image processing. The goal is to foster the exchange of knowledge and ideas and to promote the dissemination of high-quality research in this interdisciplinary area.

This Special Issue focuses on computer vision algorithms for biomedical image analysis, encompassing the development of novel methodologies and techniques for processing, analyzing, and enhancing biomedical images, including feature extraction, segmentation, object detection, and restoration. It also includes methods for image registration and alignment, qualitative analysis and measurement of anatomical structures, disease diagnosis, classification, and identification of patterns, abnormalities, and biomarkers. Moreover, it explores computer-aided detection and diagnosis and the integration of deep learning and machine learning techniques, such as CNNs and RNNs, in biomedical image processing, highlighting their applications in this domain.

Based on your expertise and previous contributions, we believe that you have the potential to make a valuable contribution to this Special Issue entitled “Computer Vision Algorithms for Biomedical Image Processing.”

Dr. Roseline Oluwaseun Ogundokun
Dr. Guanqiu Qi
Prof. Dr. Valentina De Simone
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• computer vision
• biomedical image processing
• image analysis
• feature extraction
• image segmentation
• object detection
• image enhancement
• image restoration
• anatomical structures
• morphological analysis
• disease diagnosis
• disease classification
• pattern recognition
• deep learning
• machine learning
• computer-aided detection
• medical imaging
• convolutional neural networks (CNNs)
• recurrent neural networks (RNNs)
• biomarkers
• sbnormality detection