Integration of content-based image retrieval and database management system: A case study with digital mammography

In this thesis, we proposed a new integration method for content-based image retrieval and database systems, and developed a case study on mammography retrieval to measure performance of our approach. Initially, we investigated 26 low level features in total, 17 of them exist in the literature and rest of them is our proposal for mass contour description. Additionally, we proposed a new breast mass segmentation method called Breast Mass Contour Segmentation to determine accurate breast mass contours. Next, we reviewed available mammogram datasets to evaluate our proposal, and we also developed a new mammogram dataset due to insufficient annotation level of available datasets. During development of this dataset, we developed a new ontology based annotation tool. Then, we performed series of experimentations on two different mammogram datasets to identify the best low level features, machine learning and region selection methods for breast masses. Finally, we implemented our integration approach on PostgreSQL database management system using selected low-level features and evaluate the retrieval performance. Experimentation results showed that our integration approach of content-based image retrieval and Database Management Systems worked well and successfully applied to mammography mass retrieval system as case study.