Content-based Image Retrieval in Medical Applications

Lehmann, Thomas; Güld, Mark Oliver; Thies, Christian; Fischer, Benedikt; Spitzer, Klaus; Keysers, Daniel; Ney, Hermann; Kohnen, Michael; Schubert, Henning; Wein, B.

doi:10.1055/s-0038-1633877

Cited by 234 publications

(21 citation statements)

References 29 publications

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“…For our proposed method, we compute all 13 Haralick features for five different distance values (1,3,5,7,11). This computation is done for each subregion of the spatial pyramid kernel defined above (including level 0, representing the complete image).…”

Section: Image Featuresmentioning

confidence: 99%

“…Deriving the required information from the DICOM header of the slice is often not a reliable option. Entries like 'patient position' or 'body part examined' are often imprecise or even wrong as reported by Gueld et al 1 Additionally, single slices are often embedded into report documents, so that the header information or meta data is either lost or no more accessible in an easy way. Due to these reasons, parsing DICOM headers does not yield a viable solution for obtaining the relative position of a single CT slice.…”

Section: Introductionmentioning

confidence: 99%

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CT slice localization via instance-based regression

et al. 2010

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Automatically determining the relative position of a single CT slice within a full body scan provides several useful functionalities. For example, it is possible to validate DICOM meta-data information. Furthermore, knowing the relative position in a scan allows the efficient retrieval of similar slices from the same body region in other volume scans. Finally, the relative position is often an important information for a non-expert user having only access to a single CT slice of a scan. In this paper, we determine the relative position of single CT slices via instance-based regression without using any meta data. Each slice of a volume set is represented by several types of feature information that is computed from a sequence of image conversions and edge detection routines on rectangular subregions of the slices. Our new method is independent from the settings of the CT scanner and provides an average localization error of less than 4.5 cm using leave-one-out validation on a dataset of 34 annotated volume scans. Thus, we demonstrate that instance-based regression is a suitable tool for mapping single slices to a standardized coordinate system and that our algorithm is competitive to other volume-based approaches with respect to runtime and prediction quality, even though only a fraction of the input information is required in comparison to other approaches.

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Section: Image Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CT slice localization via instance-based regression

et al. 2010

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“…Doing so can expand our project to support more widely used medical image standards, such as DICOM [24]. Therefore, with standard-formatted annotation content, images can be queried in a manner of Content-Based Image Retrieval (CBIR) which can improve accuracy and efficiency of image knowledge retrieving and analysis [25]. …”

Section: Discussionmentioning

confidence: 99%

TissueWiki<sup>Mobile</sup>: An integrative protein expression image browser for pathological knowledge sharing and annotation on a mobile device

Cheng

Hang

Wang

2010

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

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Doctors need fast and convenient access to medical data. This motivates the use of mobile devices for knowledge retrieval and sharing. We have developed TissueWikiMobile on the Apple iPhone and iPad to seamlessly access TissueWiki, an enormous repository of medical histology images. TissueWiki is a three terabyte database of antibody information and histology images from the Human Protein Atlas (HPA). Using TissueWikiMobile, users are capable of extracting knowledge from protein expression, adding annotations to highlight regions of interest on images, and sharing their professional insight. By providing an intuitive human computer interface, users can efficiently operate TissueWikiMobile to access important biomedical data without losing mobility. TissueWikiMobile furnishes the health community a ubiquitous way to collaborate and share their expert opinions not only on the performance of various antibodies stains but also on histology image annotation.

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“…IRMA is a distributed system using a central relational database, which stores administrative information about distributed objects (image data, methods of computation, and features resulting from method processing) and query processing to control parallel processing on all IRMA workstations [9].…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of the view position of chest radiographs

et al. 2003

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Automatic identification of frontal (posteroanterior/anteroposterior) vs. lateral chest radiographs is an important preprocessing step in medical imaging. A recent approach by Amura et al. (Procs SPIE 2002; 4684: 308-315) is based on manual selection and combination of about 500 radiographs to generate as much as 24 templates by pixel-wise summing up the references, and a correctness rate of 99,99 % is reported. In order to design a fully automated procedure, 1,867 images were arbitrarily selected from clinical routine as reference for this work: 1,266 in frontal and 601 in lateral view position. The size of the radiographs varies between 2,000 and 4,000 pixels in each direction. Automatic categorization is done in two steps. At first, the image is reduced substantially in size. Regardless of the initial aspect ratio, a squared version is obtained, where the number h of pixels in both directions is a power of two. In the second step, the normalized cross correlation function at the optimal displacement is used for 5-nearest-neighbor classification. Leaving-one-out experiments were performed for h = 4, 8, 16, 32, and 64 resulting in mean correctness of 92.0 %, 99.3 %, 99.3 %, 99.6 % and 99.4 %, respectively. With respect to the approach of Amura et al., these results show that the determination of the view position of chest radiographs can be fully automated and substantially simplified if the correlation function is used directly for 5-NN classification.

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Content-based Image Retrieval in Medical Applications

Cited by 234 publications

References 29 publications

CT slice localization via instance-based regression

CT slice localization via instance-based regression

TissueWiki<sup>Mobile</sup>: An integrative protein expression image browser for pathological knowledge sharing and annotation on a mobile device

Automatic detection of the view position of chest radiographs

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