Special Issue "Machine Learning for Medical Imaging"

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:
Interests: interdisciplinary research in medicine and computer science; computer-aided diagnosis; machine learning; image processing; pattern recognition

• 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) 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) 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, biomarker tracking/detection)
• Image fusion of multiple modalities, multiple phases and multiple angles
• Image retrieval (e.g., lesion similarity, context-based)
• Gene data analysis (e.g., genotype/phenotype classification/identification)
• Molecular/pathologic image analysis
• Dynamic, functional, physiologic, and anatomic imaging.

Submission

All papers should be submitted to algorithms@mdpi.org. To be published continuously until the deadline and papers will be listed together at the special issue website.

Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by Molecular Diversity Preservation International.

Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first three volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections) for papers submitted before 31 December 2010.

computer-aided diagnosis, artificial neural networks, support vector machines, manifold, classification, pattern recognition, image reconstruction, medical image analysis, statistical pattern recognition, segmentation, image fusion, image retrieval, biological imaging, multiple modalities, gene, X-ray, CT, MRI, PET, ultrasound

Title: Survey of Pixel-based Machine-Learning Algorithms for Medical Images
Author: Kenji Suzuki; E-mail: suzuki@uchicago.edu
Abstract: Machine leaning plays an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions in medical images cannot be modeled accurately by simple equations; thus, tasks in medical imaging essentially require “learning from examples.” One of the most popular use of machine-learning algorithms in medical image analysis is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or non-lesions). The task of machine-learning algorithms here is to determine “optimal” boundaries for separating classes in the multi-dimensional feature space which is formed by input features (e.g., contrast, area, and circularity) obtained from object candidates. Recently, as available computational power increased dramatically, pixel/voxel-based machine-learning algorithms (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of calculated features as input information; thus, feature calculation is not required. Since the PML can avoid errors caused by inaccurate feature calculation and segmentation, the performance of the PML is higher than common classifiers. In this paper, PML algorithms are surveyed and reviewed to make clear a) the similarities and differences within different PML algorithms and those between PML algorithms and classifiers, b) the advantages and limitations of PML algorithms, and c) their applications in medical imaging.
Keywords: Pixel-by-pixel processing; supervised filter; convolution, classification; machine learning; computer-aided diagnosis; medical image analysis

Type of Paper: Article
Title: CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI
Authors: Jing Huo, Kazunori Okada, Grace Kim, Whitney B. Pope, Jonathan G. Goldin, Jeffry R. Alger, and Matthew S. Brown; E-mail: jhuo@mednet.ucla.edu