An official publication of the International Association for Pattern Recognition

Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.

Special Issue on Open the Brain: horizons and challenges in brain image analysis
提出日: 2025-05-20

The rapid advancement of Artificial Intelligence (AI) is profoundly impacting healthcare, particularly in brain-focused medical imaging. AI- based tools are revolutionizing diagnostics by improving speed, reducing costs, and enhancing accuracy.

Brain research through image analysis is a standout area, enabling preventive diagnoses, predicting disease progression, assisting in surgeries, and facilitating non-invasive therapy studies. These advancements are transforming the field and drawing interest from technical experts and industry stakeholders.

Despite significant progress, brain imaging faces challenges such as data scarcity, lack of standardization in image acquisition, labeling issues, and other technical hurdles. The brain's complexity and neurological diseases add further difficulties. AI is addressing these challenges by enhancing data analysis, improving imaging techniques, and offering robust predictive models, driving major advancements in brain research.

This special issue aims to explore critical aspects, innovative solutions, and prospects in brain-focused medical imaging. We aim to demonstrate how AI can overcome existing challenges and unlock new possibilities in diagnosing and treating neurological conditions. The issue will highlight cutting-edge research, discuss ethical and practical considerations of AI in healthcare, and provide a comprehensive overview of the current state and future directions of AI-driven brain imaging. Building on the recent ICPR 2024 challenge (https://iplab.dmi.unict.it/mfs/ms-les-seg/), this Special Issue invites numerous submissions in the related field, extending a special invitation to the top participants from the challenge.

Topics of interest

    Machine Learning and Deep Learning Algorithms for Brain Image Analysis
    Pattern Recognition for Early Diagnosis of Neurological Diseases
    Automated Brain Image Segmentation Techniques
    Enhancing Brain Image Quality and Artifact Detection
    Generative Models for Brain Image Synthesis and Augmentation
    Foundation Models in Brain Image Analysis
    Multimodal Data Integration for Brain Pattern Recognition
    Predictive Models for Neurological Disease Progression
    Ethical Considerations and Responsible Development of AI Systems for Brain Imaging

Guest editors:

Sebastiano Battiato, PhD
University of Catania, Catania, Italy
(sebastiano.battiato@unict.it)

Francesco Guarnera, PhD
University of Catania, Catania, Italy
(francesco.guarnera@unict.it)

Alessia Rondinella, PhD
University Campus Bio-Medico of Rome, Rome, Italy
(alessia.rondinella@unicampus.it)

Alessandro Ortis, PhD
University of Catania, Catania, Italy
(alessandro.ortis@unict.it)

Daniele Ravì, PhD
University College London, London, UK
(d.ravi@ucl.ac.uk)

Manuscript submission information:

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from May 1st, 2025. When submitting your manuscript please select the article type VSI: HCBIA. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: May 1st, 2025
Submission Deadline: May 20th, 2025
Acceptance Deadline: September 30th, 2025

Keywords:

brain image analysis, medical imagina, medical generative data, neurological disease, multimodal data