Special Issue "Machine Learning for Medical Imaging 2012"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 August 2012)
Dr. Kenji Suzuki
Department of Radiology, Graduate Program in Medical Physics, and Cancer Research Center, Division of the Biological Sciences, The University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL 60637, USA
Interests: interdisciplinary research in medicine and computer science; computer-aided diagnosis; machine learning; image processing; pattern recognition
Medical imaging is an indispensable tool of patients’ healthcare in modern medicine. Machine learning plays an essential role in the medical imaging field, including medical image analysis, computer-aided diagnosis, organ/lesion segmentation, image fusion, image-guided therapy, image annotation and image retrieval, because objects such as lesions and anatomy in medical images cannot be represented accurately by simple equations; thus, tasks in medical imaging essentially require “learning from examples.” Because of its essential needs, machine learning for medical imaging is one of the most promising, rapidly growing fields. As medical imaging has been advancing with the introduction of new imaging modalities and methodologies such as cone-beam/multi-slice CT, positron-emission tomography (PET)-CT, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), electrical impedance tomography and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Areas of interest in this special issue are all aspects of machine-learning research in the medical imaging field, including, but not limited to:
- computer-aided detection/diagnosis (e.g., for lung cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
- machine learning (e.g., with support vector machines, statistical methods, manifold-space-based methods, artificial neural networks, decision tree learning, Bayesian networks, sparse dictionary learning, genetic algorithms) applications to medical images with 2D, 3D and 4D data.
- multi-modality fusion (e.g., PET-CT, projection X-ray-CT, X-ray-ultrasound)
- medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of lesions, lesion stage, organs, anatomy, status of disease and medical data
- image reconstruction (e.g., expectation maximization (EM) algorithm, statistical methods) for medical images (e.g., CT, PET, MRI, X-ray)
- biological image analysis (e.g., biological response monitoring, cell/marker tracking/detection)
- image fusion of multiple modalities, multiple phases, multiple sequences and multiple angles
- image retrieval (e.g., lesion similarity, context-based) and data mining
- gene data analysis (e.g., genotype/phenotype classification/identification)
- molecular/pathologic image analysis (e.g., PET, digital pathology)
- dynamic, functional, physiologic, and anatomic imaging.
Dr. Kenji Suzuki
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 quarterly 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 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
- computer-aided diagnosis
- support vector machines
- artificial neural networks
- decision tree learning
- Bayesian networks
- sparse dictionary learning
- genetic algorithms
- pattern recognition
- image reconstruction
- medical image analysis
- statistical pattern recognition
- image fusion
- image retrieval
- biological imaging
- multiple modalities
- digital pathology
Algorithms 2012, 5(3), 318-329; doi:10.3390/a5030318
Received: 2 June 2012; in revised form: 10 July 2012 / Accepted: 20 July 2012 / Published: 10 August 2012| Download PDF Full-text (582 KB) | Download XML Full-text
Algorithms 2013, 6(3), 512-531; doi:10.3390/a6030512
Received: 12 November 2012; in revised form: 14 August 2013 / Accepted: 15 August 2013 / Published: 21 August 2013| Download PDF Full-text (725 KB)
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Type of Paper: Article
Title: Edge Extraction from MRI and DTI Images with an Anisotropic Vector Field Flow using Divergence Map
Author: Donatella Giuliani
Affiliation: Scientific Didactic Polo of Rimini, University of Bologna, Via Angherà 22 Rimini, Italy
Abstract: The aim of this work is the extraction of edges from MRI and DTI images by a deformable contour procedure, using an external force field derived from an anisotropic flow. By evaluating the divergence of the force field, we have generated a divergence map associated with it in order to analyze the field convergence. As we know, the divergence measures the intensity of convergence or divergence of a vector field at a given point, so by means level curves of the divergence map, we have automatically selected an initial contour for the deformation process. If the initial curve includes the areas from which the vector field diverges it will be able to push it towards the edges. Furthermore the divergence map brings out the presence of curves pointing to the most significant geometric parts of boundaries corresponding to high curvature values, in this way it will be rather well defined the skeleton of the extracted object.
Keywords: DTI; MRI; edge extraction; active contour; GGVF; anisotropic diffusion; divergence map
Last update: 30 July 2012