Multi-dimensional Biomedical Data Visualization

Summary

Efficient visualization of multi-dimensional and multi-modal biomedical images for image interpretation and diagnosis.

Supervisor(s)

Professor David Feng, Associate Professor Jinman Kim

Research Location

Computer Science

Program Type

N/A

Synopsis

Public health demand and research advances are pushing healthcare into an era of transformation. With the introduction of next-generation multi-modality medical imaging scanners, new diagnostic capabilities are introduced which are resulting in tremendous advances in patient care. However, these modern scanners are restricted from its full usage capacity due to the limitation in the ability to visualize and understand the myriad of data within for diagnosis, where slice-by-slice display with simple image processing tools are currently the norm. The massive number of images (1000’s) and the complex inter-relations between the functional (PET) and anatomical (CT) images will mean that access to and assimilation of critical data within these images, by the reader and end-user (e.g. neurosurgeon, cardiothoracic surgeon) will become a major problem.The aim of this project is to investigate new possibilities from the availability of co-aligned and complementary information in multi-dimensional PET/CT image scanners, and develop new algorithms and techniques to provide improved image understanding and efficient visualization of these images.

Keywords

Computer Applications, Life and medical sciences, Image visualization, Image understanding, Volume rendering, Multi-dimensional data processing, Image processing, Image fusion, Medical Imaging, PET/CT

Opportunity ID

The opportunity ID for this research opportunity is: 315

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
• 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
• Intelligent Multimodality Molecular Image Segmentation
• Multimodality Medical Image Segmentation
• Medical Image Mining for Computer-Aided Diagnosis
• Functional Brain Image Understanding for Differential Diagnosis of Dementia
• Disease Map - Big Data driven modelling and derivation of diseases and treatment response
• Machine learning in Multiscale Image-Omics
• Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
• Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval
• Semantic-driven Multi-modal Biomedical Data Visualisation
• Computerised image analysis of musculoskeletal diagnostics and surgical planning

Other opportunities with Associate Professor Jinman Kim

• Semantic-driven Multi-modal Biomedical Data Visualisation
• Computerised image analysis of musculoskeletal diagnostics and surgical planning
• Image Representation using Multi-dimensional Biomedical Functional and Anatomical Features
• Disease Map - Big Data driven modelling and derivation of diseases and treatment response
• Machine learning in Multiscale Image-Omics