Retrieval of Brain Tumors with Region-Specific Bag-of-Visual-Words Representations in Contrast-Enhanced MRI Images

A content-based image retrieval (CBIR) system is proposed for the retrieval of T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors. In this CBIR system, spatial information in the bag-of-visual-words model and domain knowledge on the brain tumor images are considered for the representation of brain tumor images. A similarity metric is learned through a distance metric learning algorithm to reduce the gap between the visual features and the semantic concepts in an image. The learned similarity metric is then used to measure the similarity between two images and then retrieve the most similar images in the dataset when a query image is submitted to the CBIR system. The retrieval performance of the proposed method is evaluated on a brain CE-MRI dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The experimental results demonstrate that the mean average precision values of the proposed method range from 90.4% to 91.5% for different views (transverse, coronal, and sagittal) with an average value of 91.0%

A graph-based approach for the retrieval of multi-modality medical images

Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images