Content-based Retrieval of Medical Images

With the advance of multimedia and diagnostic images technologies, the number of radiographic images is increasing constantly. The medical field demands sophisticated systems for search and retrieval of the produced multimedia document. This paper presents an ongoing research that focuses on the semantic content of radiographic image documents to facilitate semantic-based radiographic image indexing and a retrieval system. The proposed model would divide a radiographic image document, based on its semantic content, and would be converted into a logical structure or a semantic structure. The logical structure represents the overall organization of information. The semantic structure, which is bound to logical structure, is composed of semantic objects with interrelationships in the various spaces in the radiographic image. Keywords—Semantic Indexing, Content-Based Retrieval, Radiographic Images, Data Model

[1]  Euripides G. M. Petrakis,et al.  Methodology for the representation, indexing and retrieval of images by content , 1993, Image Vis. Comput..

[2]  Dorin Comaniciu,et al.  Image-guided decision support system for pathology , 1999, Machine Vision and Applications.

[3]  Guergana K. Savova,et al.  Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing , 2009, Journal of Digital Imaging.

[4]  Erland Jungert,et al.  Intelligent Image Database Systems , 1996 .

[5]  Euripides G. M. Petrakis,et al.  Similarity Searching in Medical Image Databases , 1997, IEEE Trans. Knowl. Data Eng..

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

[7]  James H Thrall,et al.  Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations. , 2009, Radiology.

[8]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[9]  Yiannis Kompatsiaris,et al.  Semantic Image Analysis Using a Learning Approach and Spatial Context , 2006, SAMT.

[10]  Christine Golbreich,et al.  Towards a Hybrid System Using an Ontology Enriched by Rules for the Semantic Annotation of Brain MRI Images , 2007, RR.

[11]  S C Orphanoudakis,et al.  I2Cnet: Content-based similarity search in geographically distributed repositories of medical images. , 1996, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[12]  S. Soderland,et al.  Automatic structuring of radiology free-text reports. , 2001, Radiographics : a review publication of the Radiological Society of North America, Inc.

[13]  Daniel Sonntag,et al.  Prototyping Semantic Dialogue Systems for Radiologists , 2010, 2010 Sixth International Conference on Intelligent Environments.

[14]  Phillip C.-Y. Sheu,et al.  Image content modeling for neuroscience databases , 2002, SEKE '02.

[15]  Amarnath Gupta,et al.  Visual information retrieval , 1997, CACM.

[16]  Y. Chiaramella,et al.  Indexing, Navigation and Retrieval of Multimedia Structured Documents : the Prime Information Retrieval System , 2022 .

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

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

[19]  Jianyu Chen,et al.  [A medical image semantic modeling based on hierarchical Bayesian networks]. , 2009, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[20]  William J. Christmas,et al.  Structural Matching in Computer Vision Using Probabilistic Relaxation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

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

[23]  Johanna Vompras Towards Adaptive Ontology-Based Image Retrieval , 2005, Grundlagen von Datenbanken.