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Matthew Simpson, Md Mahmudur Rahman, Dina Demner-Fushman, Sameer Antani, George R. Thoma

Text- and Content-based Approaches to Image Retrieval for the ImageCLEF 2009 Medical Retrieval Track. Matthew Simpson, Md Mahmudur Rahman, Dina Demner-Fushman, Sameer Antani, George R. Thoma Lister Hill National Center for Biomedical Communications,

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Matthew Simpson, Md Mahmudur Rahman, Dina Demner-Fushman, Sameer Antani, George R. Thoma

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  1. Text- and Content-based Approaches to Image Retrieval for the ImageCLEF2009 Medical Retrieval Track Matthew Simpson, Md Mahmudur Rahman, Dina Demner-Fushman, Sameer Antani, George R. Thoma Lister Hill National Center for Biomedical Communications, National Library of Medicine, NIH, Bethesda, MD, USA CLEF 2009

  2. Retrieval tasks and approaches • ITI project long term goal • Find a way to combine image and text features so that the whole is greater than the sum of its parts • Ad-hoc image retrieval • Text-based • Image content-based • Automatic mixed • Relevance feedback mixed • Case-based document retrieval • Text-based

  3. Text-based approach • Indexing: • Create image documents for ad-hoc image retrieval • Create surrogate documents for case-based retrieval • Index using Essie • term normalization using the SPECIALIST Lexicon • query expansion based on UMLS synonymy • term weighting based on location in the document • Phrase-based search

  4. Text documents • Image document • Title and caption provided by organizers • Mention extracted from paper • MEDLINE citation (abstract +MeSH) • PICO frame of the caption + image modality (structured caption summary) • Surrogate document • MEDLINE citation • caption, mention, and structured caption summary of each image contained in the article

  5. Text retrieval • PICO-based structured query and case representation • <topicID>19</topicID> <description>Crohn's disease CT</description> • <modality essieExp="false">ct</modality> <modSyn>c.a.t.</modSyn><modSyn>cat</modSyn><modSyn>computerised axial tomography</modSyn>…. • <cond essieExp="true">Crohn's disease</cond><condPN>crohn disease</condPN><condSyn>Regional enteritis</condSyn> <condSyn>eleocolitis</condSyn><condSyn>Cicatrizing enterocolitis</condSyn><condSyn>granulomatous enteritis</condSyn><condSyn>INFLAMMATORY BOWEL DISEASE</condSyn><condSyn>regional enterocolitis</condSyn> …

  6. CBIR - Image feature representation • Concepts - color and texture patches from local image regions • Low-level global features • Color (Color Layout Descriptor, MPEG-7) • Edge (histogram of local edge distribution and direction) • Texture (grey level co-occurrence matrix) • Average grey level (256-dimensional vector of blocks in image normalized to gray-level 64x64) • Lucene (LIRE)-based Color Edge Direction Descriptor and Fuzzy Color Texture Histogram

  7. Image similarity computation • Category-specific • Determine image category (training set of 5000 images manually assigned to 32 mutually exclusive categories) • Use category-specific weights in linear similarity matching • Relevance feedback • Feature weights updated using images judged relevant

  8. Combining text and image • Based on text search results, • Compute mean vector of top 5 retrieved images, use as input to category-specific retrieval • Select 3-5 relevant images manually, use as input to category-specific retrieval • Re-rank text retrieval results using visual retrieval scores • Provide feedback using all retrieval results, • expand query using image documents • Pad selected relevant images with new retrieval results

  9. Relevance Feedback

  10. Results category- specific RF text re- ranked BRF RF RF+QE case-based visual mixed

  11. Image-text search engine

  12. Thank you! Questions?

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