Region of Interest Queries in CT Scans

Cavallaro, Alexander; Graf, Franz; Kriegel, Hans‐Peter; Schubert, Matthias; Thoma, Marisa

doi:10.1007/978-3-642-22922-0_5

Cited by 9 publications

(12 citation statements)

References 8 publications

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“…The simplest relevance function, (LIN) rel p lin (s i, q ) = 1, is equivalent to applying a weighted linear interpolation. In [13], we used the inverse absolute difference, (ABS) as a relevance term, as it showed to be more stable than the squared inverse difference (SQR) used in [15]. Other, more complex relevance functions showed to be even less suited for obtaining a reliable height interpolation.…”

Section: Mapping Functionsmentioning

confidence: 99%

“…This approach maps a single slice s i, q into a height scale via k-nearest neighbor regression using an image descriptor for s i, q . We already use this method in a partial retrieval algorithm detailed in [13], which at the time of writing did not yet make use of a semantic localization atlas.…”

Section: Mapping Functionsmentioning

confidence: 99%

“…Landmarks are anatomically-relevant points within the body of low spatial variance. If no landmarks are available as matching points, we artificially generate some by using instance-based regression as in [13,14]. In order to query a series for a particular semantic concept, our approach provides a body atlas storing the height distribution for multiple organs and landmarks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Localization-driven Partial Image Retrieval in CT Series

Cavallaro

Kriegel²,

Petri

et al. 2012

Methods Inf Med

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show abstract

Section: Mapping Functionsmentioning

confidence: 99%

Section: Mapping Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Localization-driven Partial Image Retrieval in CT Series

Cavallaro

Kriegel²,

Petri

et al. 2012

Methods Inf Med

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show abstract

“…Especially with anatomic segmentation information available [3], applications are possible, where different parts of a volume, like the heart or the lung, can be decoded with a higher quality while the remaining volume can be decoded in a coarser representation. Thus, only the important information has to be decoded.…”

Section: Introductionmentioning

confidence: 99%

3-D mesh compensated wavelet lifting for 3-D+t medical CT data

Schnurrer

Richter

Seiler

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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For scalable coding, a high quality of the lowpass band of a wavelet transform is crucial when it is used as a downscaled version of the original signal. However, blur and motion can lead to disturbing artifacts. By incorporating feasible compensation methods directly into the wavelet transform, the quality of the lowpass band can be improved. The displacement in dynamic medical 3-D+t volumes from Computed Tomography is mainly given by expansion and compression of tissue over time and can be modeled well by mesh-based methods. We extend a 2-D mesh-based compensation method to three dimensions to obtain a volume compensation method that can additionally compensate deforming displacements in the third dimension. We show that a 3-D mesh can obtain a higher quality of the lowpass band by 0.28 dB with less than 40% of the model parameters of a comparable 2-D mesh. Results from lossless coding with JPEG 2000 3D and SPECK3D show that the compensated subbands using a 3-D mesh need about 6% less data compared to using a 2-D mesh.

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“…The advantage of this framework is that it is generic and can be extended to other modalities. Thoma et al [9] proposed a method for ROI queries in CT scans. This approach first employed instancebased regression in combination with interpolation techniques for mapping the scan slides to a height model of human body.…”

Section: Introductionmentioning

confidence: 99%

Content-based white blood cell retrieval on bright-field pathology images

Gensure

Foran

et al. 2013

SPIE Proceedings

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The purpose of this work was to evaluate a newly developed content-based retrieval approach for characterizing a range of different white blood cells from a database of imaged peripheral blood smears. Specimens were imaged using a 20x magnification to provide adequate resolution and sufficiently large field of view. The resulting database included a test ensemble of 96 images (1000x1000 pixels each). In this work, we propose a four-step content-based retrieval method and evaluate its performance. The content-based image retrieval (CBIR) method starts from white blood cell identification, followed by three sequential steps including coarse-searching, refined searching, and finally mean-shift clustering using a hierarchical annular histogram (HAH). The prototype system was shown to reliably retrieve those candidate images exhibiting the highest-ranked (most similar) characteristics to the query. The results presented here show that the algorithm was able to parse out subtle staining differences and spatial patterns and distributions for the entire range of white blood cells under study. Central to the design of the system is that it capitalizes on lessons learned by our team while observing human experts when they are asked to carry out these same tasks.Keywords: Content-based image retrieval, white blood cells INTRODUCTIONThe exponential growth of images and video in last decade has resulted in an increasing need for efficient contentbased image retrieval (CBIR), which enables investigators to detect and locate similar images in large collections given an example query. For medical diagnostic assistance, several state-of-the-art CBIR systems [1][2][3] have been designed to support the processing of queries across separate images. However, often times users are interested in sub-region searching, usually to identify an image patch exhibiting specific patterns or structures or containing an object which is similar to one in the query patch. Given this sub-region, the system should be able to return other patches within the same set of images which contain localized sub-regions exhibiting similar features. In practice, this approach makes it possible for a pathologist to select an area or object of interest within a digitized bio-specimen as a query to reliably search corresponding regions in either the same bio-specimen or within different specimens from large cohorts of patients having the same disease. The results can then be used to enable physicians to draw comparisons among the samples in order to make informed decision regarding the prognosis and preferred treatment regimens.Recently researchers have proposed many state-of-the-art methods to perform CBIR related to both natural and medical images. Luo and Nascimento [4] introduced relevance feedback by applying a tile re-weighting approach to assign penalties to tiles that compose database images and update the penalties for all retrieved images within each iteration. This procedure is time consuming due to the reliance of the algorithm on feedback learning. T...

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Region of Interest Queries in CT Scans

Cited by 9 publications

References 8 publications

Semantic Localization-driven Partial Image Retrieval in CT Series

Semantic Localization-driven Partial Image Retrieval in CT Series

3-D mesh compensated wavelet lifting for 3-D+t medical CT data

Content-based white blood cell retrieval on bright-field pathology images

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Region of Interest Queries in CT Scans

Cited by 9 publications

References 8 publications

Semantic Localization-driven Partial Image Retrieval in CT Series

Semantic Localization-driven Partial Image Retrieval in CT Series

3-D mesh compensated wavelet lifting for 3-D&#x002B;t medical CT data

Content-based white blood cell retrieval on bright-field pathology images

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Product

Resources

About

3-D mesh compensated wavelet lifting for 3-D+t medical CT data