Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval

Summary

Intelligent object-based characterization and extraction of pathologic volumetric texture patterns for biomedical image classification and retrieval.

Supervisor(s)

Associate Professor Tom Weidong Cai, Professor David Feng

Research Location

Computer Science

Program Type

N/A

Synopsis

Texture is a powerful discriminating visual feature which has been widely used in pattern recognition and computer vision for identifying visual patterns with properties of homogeneity that cannot result from the presence of only a single color or intensity, and presents almost everywhere in nature – the size of the image patch, the number of distinguishable grey-level primitives and the spatial relationships between these primitives, are all interrelated elements which characterize a texture pattern. Most of biomedical images acquired and represented in gray scale are often highly textured, and consequently, examination of biomedical images usually requires interpretation of organ / tissue / lesion appearance, i.e., the local intensity variations, based on different texture properties such as smoothness, coarseness, regularity, and homogeneity. Since texture acquires such distinguished importance, it is becoming one of the most commonly used characteristics in biomedical image classification and retrieval. This project aims to conduct the study of the characterization and extraction of intrinsic volumetric textures in multiple biomedical object levels such as anatomic organs, tissue regions, and focal lesion objects, with the design of proper statistical texture modeling approaches, to provide great discriminatory information which is of paramount importance to successful classification and retrieval of biomedical imaging data, and to offer intelligent diagnosis via comparing past and current biomedical images associated with pathologic conditions in case-based reasoning or evidence-based medicine.

Keywords

Computer Applications, Life and medical sciences, Volumetric texture, Statistical texture analysis and modeling, Content-based image retrieval, Biomedical imaging, Computer assisted diagnosis (CAD)

Opportunity ID

The opportunity ID for this research opportunity is: 312

Other opportunities with Associate Professor Tom Weidong Cai

• Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
• Semantic Feature Description and Similarity Measurement for Large-scale Image Search
• Neuroimaging Computing for Early Detection of Alzheimer’s Disease
• Feature-Centric Content Analysis for Bioimaging Informatics
• Visual Feature Extraction for Image Pattern Classification
• Kinetic Characterization and Mapping for Whole-body Molecular Image Retrieval

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
• 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
• Semantic-driven Multi-modal Biomedical Data Visualisation
• Computerised image analysis of musculoskeletal diagnostics and surgical planning