Dr. Matthias Elter Profile

Dr. Matthias Elter

Technical Lead, Imaging Solutions at Siemens AG

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Publications (9)

Proceedings Article | 6 March 2013 Paper

Influence of metal segmentation on the quality of metal artifact reduction methods

Maik Stille, Bärbel Kratz, Jan Müller, Nicole Maass, Ingo Schasiepen, Matthias Elter, Imke Weyers, Thorsten Buzug

Proceedings Volume 8668, 86683C (2013) https://doi.org/10.1117/12.2006810

KEYWORDS: Metals, Image segmentation, Image quality, Image enhancement, Computed tomography, Medical imaging, Medical diagnostics, Data acquisition, CT reconstruction, Teeth

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Proceedings Article | 6 March 2013 Paper

Fast iterative beam hardening correction based on frequency splitting in computed tomography

Qiao Yang, Matthias Elter, Ingo Schasiepen, Nicole Maass, Joachim Hornegger

Proceedings Volume 8668, 86682J (2013) https://doi.org/10.1117/12.2007808

KEYWORDS: Reconstruction algorithms, Image segmentation, X-rays, Beam splitters, Image resolution, Computed tomography, Signal attenuation, Computer simulations, Photons, X-ray computed tomography

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Proceedings Article | 9 March 2011 Paper

Classification of mammographic masses: use and influence of a bilateral-filter-based flat-texture approach

Florian Wagner, Matthias Elter, Rüdiger Schulz-Wendtland, Thomas Wittenberg

Proceedings Volume 7963, 796324 (2011) https://doi.org/10.1117/12.878004

KEYWORDS: Gaussian filters, Digital filtering, Fourier transforms, Computer aided diagnosis and therapy, Image filtering, Image classification, Medical imaging, Current controlled current source, Fiber Bragg gratings, Diagnostics

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Proceedings Article | 9 March 2010 Paper

Classification of mammographic masses: influence of regions used for feature extraction on the classification performance

Florian Wagner, Thomas Wittenberg, Matthias Elter

Proceedings Volume 7624, 76242H (2010) https://doi.org/10.1117/12.844675

KEYWORDS: Feature extraction, Computer aided diagnosis and therapy, Medical imaging, Current controlled current source, Neodymium, Human-computer interaction, Diagnostics, Image segmentation, Receivers, Mammography

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Computer-assisted diagnosis (CADx) for the characterization of mammographic masses as benign or malignant has a very high potential to help radiologists during the critical process of diagnostic decision making. By default, the characterization of mammographic masses is performed by extracting features from a region of interest (ROI) depicting the mass. To investigate the influence of the region on the classification performance, textural, morphological, frequency- as well as moment-based features are calculated in subregions of the ROI, which has been delineated manually by an expert. The investigated subregions are (a) the semi-automatically segmented area which includes only the core of the mass, (b) the outer border region of the mass, and (c) the combination of the outer and the inner border region, referred to as mass margin. To extract the border region and the margin of a mass an extended version of the rubber band straightening transform (RBST) was developed. Furthermore, the effectiveness of the features extracted from the RBST transformed border region and mass margin is compared to the effectiveness of the same features extracted from the untransformed regions. After the feature extraction process a preferably optimal feature subset is selected for each feature extractor. Classification is done using a k-NN classifier. The classification performance was evaluated using the area A_z under the receiver operating characteristic curve. A publicly available mammography database was used as data set. Results showed that the manually drawn ROI lead to superior classification performances for the morphological feature extractors and that the transformed outer border region and the mass margin are not suitable for moment-based features but yield to promising results for textural and frequency-based features. Beyond that the mass margin, which combines the inner and the outer border region, leads to better classification performances compared to the outer border region for its own.

Proceedings Article | 9 March 2010 Paper

An improved method for segmentation of mammographic masses

Matthias Elter, Christian Held

Proceedings Volume 7624, 76242I (2010) https://doi.org/10.1117/12.843227

KEYWORDS: Computer aided diagnosis and therapy, Image segmentation, Medical imaging, Current controlled current source, Computing systems, Absorbance, Tissues, Databases

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Proceedings Article | 13 March 2009 Paper

A multi-image approach to CADx of breast cancer with integration into PACS

Matthias Elter, Thomas Wittenberg, Rüdiger Schulz-Wendtland, Thomas Deserno

Proceedings Volume 7264, 72640X (2009) https://doi.org/10.1117/12.810983

KEYWORDS: Computer aided diagnosis and therapy, Breast cancer, Picture Archiving and Communication System, Mammography, Magnetic resonance imaging, Telecommunications, Medical imaging, Ultrasonography, Breast, Digital breast tomosynthesis

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While screening mammography is accepted as the most adequate technique for the early detection of breast cancer, its low positive predictive value leads to many breast biopsies performed on benign lesions. Therefore, we have previously developed a knowledge-based system for computer-aided diagnosis (CADx) of mammographic lesions. It supports the radiologist in the discrimination of benign and malignant lesions. So far, our approach operates on the lesion level and employs the paradigm of content-based image retrieval (CBIR). Similar lesions with known diagnosis are retrieved automatically from a library of references. However, radiologists base their diagnostic decisions on additional resources, such as related mammographic projections, other modalities (e.g. ultrasound, MRI), and clinical data. Nonetheless, most CADx systems disregard the relation between the craniocaudal (CC) and mediolateral-oblique (MLO) views of conventional mammography. Therefore, we extend our approach to the full case level: (i) Multi-frame features are developed that jointly describe a lesion in different views of mammography. Taking into account the geometric relation between different images, these features can also be extracted from multi-modal data; (ii) the CADx system architecture is extended appropriately; (iii) the CADx system is integrated into the radiology information system (RIS) and the picture archiving and communication system (PACS). Here, the framework for image retrieval in medical applications (IRMA) is used to support access to the patient's health care record. Of particular interest is the application of the proposed CADx system to digital breast tomosynthesis (DBT), which has the potential to succeed digital mammography as the standard technique for breast cancer screening. The proposed system is a natural extension of CADx approaches that integrate only two modalities. However, we are still collecting a large enough database of breast lesions with images from multiple modalities to evaluate the benefits of the proposed approach on.

Proceedings Article | 3 March 2009 Paper

Incorporating a segmentation routine for mammographic masses into a knowledge-based CADx approach

Matthias Elter, Tobias Bergen

Proceedings Volume 7260, 726025 (2009) https://doi.org/10.1117/12.810981

KEYWORDS: Computer aided diagnosis and therapy, Image segmentation, Tissues, Medical imaging, Current controlled current source, Computing systems, Breast, Mammography, Feature extraction, Absorbance

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Proceedings Article | 17 March 2008 Paper

Semiautomatic segmentation for the computer aided diagnosis of clustered microcalcifications

Matthias Elter, Christian Held

Proceedings Volume 6915, 691524 (2008) https://doi.org/10.1117/12.770146

KEYWORDS: Particles, Image segmentation, Computer aided diagnosis and therapy, Mammography, Databases, Feature extraction, Digital mammography, Breast, Biopsy, Cancer

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Proceedings Article | 17 March 2008 Paper

A knowledge-based approach to CADx of mammographic masses

Matthias Elter, Erik Haßlmeyer

Proceedings Volume 6915, 69150L (2008) https://doi.org/10.1117/12.770135

KEYWORDS: Computer aided diagnosis and therapy, Breast, Mammography, Biopsy, Computing systems, Feature extraction, Databases, Breast cancer, Feature selection, Binary data

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Showing 5 of 9 publications

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