Medical imaging is indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, medical image analysis, organ/lesion segmentation, image registration, and image-guided therapy. Because of large variations and complexity, it is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require learning from examples for accurate representation of data and knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. As medical imaging has been advancing with the introduction of new imaging modalities and methodologies such as cone-beam/multislice CT, positron-emission tomography (PET)/CT, tomosynthesis, and diffusion-weighted MRI, new machine-learning algorithms/applications are demanded in the medical imaging field.

The main aim of this special issue is to help advance scientific research within the broad field of medical imaging and machine learning. This special issue is planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010), which is the first workshop on this topic, being held with International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010). This special issue focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in medical imaging. We are looking for original, high-quality submissions on innovative research and development in all aspects of machine learning in medical imaging including, but not limited to:

• Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
• Machine learning (e.g., with support vector machines, kernel methods, statistical methods, probabilistic modeling, manifold-space-based methods, artificial neural networks) applications to medical images with 2D, 3D, and 4D data
• Medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of anatomic structures and lesion
• Multimodality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound) for diagnosis, image analysis, and image-guided intervention
• Image reconstruction (e.g., expectation maximization and statistical methods)
• Image retrieval (e.g., context-based retrieval, lesion similarity)
• Cellular image analysis (e.g., genotype, phenotype, classification, cell tracking)
• Biological image analysis (e.g., biological response monitoring, biomarker detection and tracking)
• Image fusion (e.g., multiple modalities, multiple phases, multiple angles)

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/ijbi/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable: