Intelligent Multimodality Molecular Image Segmentation
Automated segmentation of major structures, tissues, and volume of interest (VOI) from PET/CT images new knowledge from information technologies.
he ever-increasing amounts of molecular images produced in hospitals around the world require intensive efforts of proficient physicians to manually delineate structures and region-of-interest (ROI) from them. This manual processing is laborious and operator dependent and thus prone to reproducibility errors. Automated image segmentation is traditionally a difficult task in image processing. However, the newly emerged multimodality molecular images have brought great potentials to the research of image segmentation, since they contain complementary information from diverse imaging modalities. The aim of this project is to use PET-CT images as our case study to investigate novel segmentation approaches for multimodality molecular images.
The output of this project will be a segmentation algorithm toolbox, which will facilitate molecular image visualization, retrieval, and computer aided diagnosis (CAD). Students involved in the projects will have the opportunity to work in RPA hospital for a certain period of time depending on the progress and necessarily. By finishing this project, students will learn some state of the art technologies in both medical imaging and multimedia.
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The opportunity ID for this research opportunity is: 385
Other opportunities with Professor David Feng
- Advanced computer modelling of biological systems using insight knowledge
- Discovery of new image-derived features for computer-aided diagnosis
- Automated 3-Dimensional Biomedical Registration for Whole-body Images from Combined PET/CT Scanners - Automatic Registration for 3D Whole-body Images from Combined PET/CT Scanners
- Deformable Registration for Temporal Lung Volumes
- Kinetic Characterization and Mapping for Whole-body Molecular Image Retrieval
- Multi-dimensional Biomedical Data Visualization
- Image Representation using Multi-dimensional Biomedical Functional and Anatomical Features
- Automatic Image Content Annotation
- Web Image Annotation
- Data Management for Automated Identification and Classification of Plant Images
- Novel Image Retouching Techniques
- Automatic Video Content Annotation
- Intelligent Access to Digital TV Content
- Semantic Multimedia Information Retrieval
- Multimedia Streaming with Peer-to-Peer Techniques
- Multimodality Medical Image Segmentation
- Medical Image Mining for Computer-Aided Diagnosis
- Functional Brain Image Understanding for Differential Diagnosis of Dementia
- Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
- Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval