OBJECTIVES: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. METHODS: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. RESULTS: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.
OBJECTIVES: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. METHODS: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. RESULTS: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.
Authors: Petra Welter; Christian Hocken; Thomas M Deserno; Christoph Grouls; Rolf W Günther Journal: Int J Comput Assist Radiol Surg Date: 2010-04-09 Impact factor: 2.924
Authors: Fan Zhang; Yang Song; Weidong Cai; Sidong Liu; Siqi Liu; Sonia Pujol; Ron Kikinis; Yong Xia; Michael J Fulham; David Dagan Feng Journal: IEEE Trans Biomed Eng Date: 2015-09-10 Impact factor: 4.538
Authors: Thomas M Deserno; Mark O Güld; Bartosz Plodowski; Klaus Spitzer; Berthold B Wein; Henning Schubert; Hermann Ney; Thomas Seidl Journal: J Digit Imaging Date: 2007-05-12 Impact factor: 4.056
Authors: Benedikt Fischer; Petra Welter; Rolf W Günther; Thomas M Deserno Journal: Int J Comput Assist Radiol Surg Date: 2011-06-14 Impact factor: 2.924