| The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic similarity. The effectiveness of the LSA retrieval was evaluated based on precision, recall, and F-score.;This work extended the application of LSA to high-resolution CT radiology images. The images were chosen for their unique characteristics and their importance in medicine. Because CT images are intensity-only, they carry less information than color images. They typically have greater noise, higher intensity, greater contrast, and fewer colors than a raw RGB image. The study targeted level of intensity for image features extraction.;The focus of this work was a formal evaluation of the LSA method in the context of large number of high-resolution radiology images. The study reported on preprocessing and retrieval time and discussed how reduction of the feature set size affected the results.;LSA is an information retrieval technique that is based on the vector-space model. It works by reducing the dimensionality of the vector space, bringing similar terms and documents closer together. Matlab software was used to report on retrieval and preprocessing time.;In determining the minimum size of concept space, it was found that the best combination of precision, recall, and F-score was achieved with 250 concepts (k = 250). This research reported precision of 100% on 100% of the queries and recall close to 90% on 100% of the queries with k=250. Selecting a higher number of concepts did not improve recall and resulted in significantly increased computational cost. |
