Special Issue "Machine Learning for Medical Imaging 2012"

Guest Editor
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
Website: http://suzukilab.uchicago.edu/
E-Mail: suzuki@uchicago.edu
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
Guest Editor

Submission

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• computer-aided diagnosis
• support vector machines
• artificial neural networks
• manifold
• decision tree learning
• Bayesian networks
• sparse dictionary learning
• genetic algorithms
• classificatio
• pattern recognition
• image reconstruction
• registration
• medical image analysis
• statistical pattern recognition
• segmentation
• image fusion
• image retrieval
• biological imaging
• multiple modalities
• gene
• X-ray
• CT
• MRI
• PET
• Ultrasound
• digital pathology

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