Similarity-Based Retrieval for Biomedical Applications

Similarity-based image retrieval, which has become an important area of computer vision, is a part of the case-based reasoning scenario. In similarity-based retrieval, a query image is provided and similar images from a database are retrieved, usually in order of similarity. In this chapter, we discuss the use of similarity-based retrieval for biomedical data. In particular, we describe three different applications that retrieve various types of image and signal data using similarity functions, including brain data (fMRI images and single-unit recording signals), mouse eye data (slit lens images), and skull data (CT scans). We define the similarity measures used in these applications and then discuss a unified query framework for multimedia data in general.

[1]  Sharad Mehrotra,et al.  The hybrid tree: an index structure for high dimensional feature spaces , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[2]  Linda G. Shapiro,et al.  A Flexible Image Database System for Content-Based Retrieval , 1999, Comput. Vis. Image Underst..

[3]  E Helene Sage,et al.  Hevin/SC1, a matricellular glycoprotein and potential tumor-suppressor of the SPARC/BM-40/Osteonectin family. , 2004, The international journal of biochemistry & cell biology.

[4]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[5]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[6]  Salvador Ruiz-Correa,et al.  New Scaphocephaly Severity Indices of Sagittal Craniosynostosis: A Comparative Study with Cranial Index Quantifications , 2006, The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association.

[7]  Chi-Ren Shyu,et al.  A fast protein structure retrieval system using image-based distance matrices and multidimensional index , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.

[8]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Proceedings of International Conference on Image Processing.

[9]  Christos Faloutsos,et al.  Fast and Effective Retrieval of Medical Tumor Shapes , 1998, IEEE Trans. Knowl. Data Eng..

[10]  L.G. Shapiro,et al.  Symbolic Shape Descriptors for Classifying Craniosynostosis Deformations from Skull Imaging , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  Aleksandra Mojsilovic,et al.  Semantic based categorization, browsing and retrieval in medical image databases , 2002, Proceedings. International Conference on Image Processing.

[12]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[13]  Hermann Ney,et al.  FIRE - Flexible Image Retrieval Engine: ImageCLEF 2004 Evaluation , 2004, CLEF.

[14]  Don R. Hush,et al.  Query by image example: The CANDID approach , 1995 .

[15]  Bir Bhanu,et al.  Probabilistic Feature Relevance Learning for Content-Based Image Retrieval , 1999, Comput. Vis. Image Underst..

[16]  Petra Perner,et al.  CONCEPTUAL CLUSTERING AND CASE GENERALIZATION OF TWO‐DIMENSIONAL FORMS , 2006, Comput. Intell..

[17]  Dong Xu,et al.  ProteinDBS: a real-time retrieval system for protein structure comparison , 2004, Nucleic Acids Res..

[18]  Linda G. Shapiro,et al.  A new neuron spike sorting method using maximal overlap discrete wavelet transform and rotated principal component analysis , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[19]  Thomas S. Huang,et al.  Supporting content-based queries over images in MARS , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[20]  Kian-Lee Tan,et al.  Rapid 3D protein structure database searching using information retrieval techniques , 2004, Bioinform..

[21]  Linda G. Shapiro,et al.  Classifying craniosynostosis deformations by skull shape imaging , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[22]  M W Vannier,et al.  Cranial base changes following surgical treatment of craniosynostosis. , 1986, The Cleft palate journal.

[23]  Petra Perner.  An architecture for a CBR image segmentation system , 1999 .

[24]  Shih-Fu Chang,et al.  Visually Searching the Web for Content , 1997, IEEE Multim..

[25]  Xiaoning Qian,et al.  Optimally adapted indexing trees for medical image databases , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[26]  C. Bonaïti‐pellié,et al.  Genetic study of scaphocephaly. , 1996, American journal of medical genetics.

[27]  Patrice Degoulet,et al.  Case Based Diagnosis in Histopathology of Breast Tumours , 1998, MedInfo.

[28]  Michael Stonebraker,et al.  Chabot: Retrieval from a Relational Database of Images , 1995, Computer.

[29]  H.D. Tagare.  Increasing retrieval efficiency by index tree adaptation , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[30]  C R Rao.  Geometry of circular vectors and pattern recognition of shape of a boundary. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[31]  James S. Duncan,et al.  Synthesis of Research: Medical Image Databases: A Content-based Retrieval Approach , 1997, J. Am. Medical Informatics Assoc..

[32]  Robert T. Macura,et al.  MacRad: Radiology Image Resource with a Case-Based Retrieval System , 1995, ICCBR.

[33]  A. Shuper,et al.  The incidence of isolated craniosynostosis in the newborn infant. , 1985, American journal of diseases of children.

[34]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[35]  Ricky K. Taira,et al.  Knowledge-Based Image Retrieval with Spatial and Temporal Constructs , 1998, IEEE Trans. Knowl. Data Eng..

[36]  Lei Zheng,et al.  Design and analysis of a content-based pathology image retrieval system , 2003, IEEE Transactions on Information Technology in Biomedicine.

[37]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[38]  Tom Minka,et al.  Interactive learning with a "Society of Models" , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[39]  Lionel Brunie,et al.  Content-based and metadata retrieval in medical image database , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[40]  John R. Smith,et al.  Image Classification and Querying Using Composite Region Templates , 1999, Comput. Vis. Image Underst..

[41]  A. Kak,et al.  Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. , 2003, Radiology.

[42]  Petra Perner,et al.  Case-base maintenance by conceptual clustering of graphs , 2006, Eng. Appl. Artif. Intell..

[43]  Linda G. Shapiro,et al.  Detection of neural activity in event-related fMRI using wavelets and dynamic time warping , 2003, SPIE Optics + Photonics.

[44]  Arnold W. M. Smeulders,et al.  Content-Based Image Retrieval , 2004 .

[45]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[46]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[47]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Carla E. Brodley,et al.  ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases , 1999, Comput. Vis. Image Underst..

[49]  Takeo Kanade,et al.  Semantic-based Biomedical Image Indexing and Retrieval , 2003 .

[50]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[51]  EURIPIDES G. M. PETRAKIS.  Content-Based Retrieval of Medical Images , 2002 .

[52]  Gerold Porenta,et al.  Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams , 1997, Artif. Intell. Medicine.

[53]  C C Howe,et al.  Disruption of the Sparc locus in mice alters the differentiation of lenticular epithelial cells and leads to cataract formation. , 1999, Experimental eye research.

[54]  Rudolf Hanka,et al.  Histological image retrieval based on semantic content analysis , 2003, IEEE Transactions on Information Technology in Biomedicine.

[55]  M W Vannier,et al.  Surgical management of sagittal synostosis. A quantitative evaluation of two techniques. , 1991, Neurosurgery clinics of North America.

[56]  Carla E. Brodley,et al.  ASSERT: A PHYSICIAN-IN-THE-LOOP CONTENT-BASED IMAGE RETRIEVAL SYSTEM FOR HRCT IMAGE DATABASES , 1999 .