Dr. Thomas Wittenberg Profile

Dr. Thomas Wittenberg

Chief Scientist / Chief Scientifc Officer at Fraunhofer-IIS

SPIE Involvement:

Author

Area of Expertise:

Endoscopy & Laparoscopic Imaging , Surgical Procedures , Digoital Pathology , Textture Analysis , Computer Assisted Diagnosis , Medical Image Processing

Websites:

Company Website

Publications (16)

SPIE Journal Paper | 14 March 2022 Open Access

Open Access

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

Frauke Wilm, Michaela Benz, Volker Bruns, Serop Baghdadlian, Jakob Dexl, David Hartmann, Petr Kuritcyn, Martin Weidenfeller, Thomas Wittenberg, Susanne Merkel, Arndt Hartmann, Markus Eckstein, Carol Immanuel Geppert

JMI, Vol. 9, Issue 02, 027501, (March 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.2.027501

KEYWORDS: Tumors, Tissues, Image segmentation, Cartography, Colorectal cancer, Image classification, Image resolution, Visualization, Colon, Pathology

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Proceedings Article | 1 March 2017 Paper

Paper

Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution

Wolfgang Aichinger, Sebastian Krappe, A. Enis Cetin, Rengul Cetin-Atalay, Aysegül Üner, Michaela Benz, Thomas Wittenberg, Marc Stamminger, Christian Münzenmayer

Proceedings Volume 10140, 101400N (2017) https://doi.org/10.1117/12.2254036

KEYWORDS: Convolutional neural networks, Cancer, Neural networks, Stem cells, Tissues, Statistical analysis, Medicine, Oncology, Databases, Machine learning, Pattern recognition, Pathology, RGB color model, Image segmentation, Image classification, Liver

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Proceedings Article | 1 March 2017 Paper

Paper

Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks

Sebastian Krappe, Michaela Benz, Alexander Gryanik, Egbert Tannich, Christine Wegner, Marc Stamminger, Thomas Wittenberg, Chrisitan Münzenmayer

Proceedings Volume 10140, 101400B (2017) https://doi.org/10.1117/12.2249845

KEYWORDS: Microscopes, Computing systems, Blood, Neural networks, Microscopy, Diagnostic tests, Medicine, Morphological analysis, Computer aided diagnosis and therapy, Convolutional neural networks, Databases, RGB color model, Visualization, Image segmentation

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Proceedings Article | 24 March 2016 Paper

Paper

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

Sebastian Krappe, Thomas Wittenberg, Torsten Haferlach, Christian Münzenmayer

Proceedings Volume 9785, 97853C (2016) https://doi.org/10.1117/12.2216037

KEYWORDS: Image segmentation, Leukemia, Morphological analysis, Image classification, Microscopy, Computing systems, Plasma, Machine learning, Hematology, Classification systems, Image analysis, Color image segmentation, Data analysis, RGB color model, Tolerancing, Visualization, Feature extraction

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Proceedings Article | 23 March 2016 Paper

Paper

Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

Oğuzhan Oğuz, Cem Emre Akbaş, Maen Mallah, Kasım Taşdemir, Ece Akhan Güzelcan, Christian Muenzenmayer, Thomas Wittenberg, Ayşegül Üner, A. Cetin, Rengül Çetin Atalay

Proceedings Volume 9791, 97910Y (2016) https://doi.org/10.1117/12.2216113

KEYWORDS: Cancer, Stem cells, Neural networks, Image classification, Tissues, Feature extraction, RGB color model, Algorithm development, Liver, Liver cancer, Detection and tracking algorithms, Image fusion

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Proceedings Article | 20 March 2015 Paper

Paper

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

Sebastian Krappe, Michaela Benz, Thomas Wittenberg, Torsten Haferlach, Christian Münzenmayer

Proceedings Volume 9414, 94143I (2015) https://doi.org/10.1117/12.2081946

KEYWORDS: Bone, Image segmentation, Image classification, Leukemia, Feature extraction, Morphological analysis, Plasma, Microscopes, Image acquisition, RGB color model

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Proceedings Article | 15 March 2013 Paper

Paper

A graph-based approach for local and global panorama imaging in cystoscopy

Tobias Bergen, Thomas Wittenberg, Christian Münzenmayer, Chi Chiung Grace Chen, Gregory Hager

Proceedings Volume 8671, 86711K (2013) https://doi.org/10.1117/12.2008174

KEYWORDS: Panoramic photography, Bladder, Video, Cystoscopy, Electroluminescent displays, Endoscopes, Distortion, In vivo imaging, Visualization, Surgery

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Proceedings Article | 9 March 2011 Paper

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

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 | 27 March 2009 Paper

Paper

Optimized graph-based mosaicking for virtual microscopy

Dirk Steckhan, Thomas Wittenberg

Proceedings Volume 7259, 72592L (2009) https://doi.org/10.1117/12.812144

KEYWORDS: Microscopy, Robotics, Medical imaging, Image processing, Current controlled current source, Microscopes

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Proceedings Article | 13 March 2009 Paper

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 | 28 February 2009 Paper

Paper

Narrow-band imaging for the computer assisted diagnosis in patients with Barrett's esophagus

Andreas Kage, Martin Raithel, Steffen Zopf, Thomas Wittenberg, Christian Münzenmayer

Proceedings Volume 7260, 72603S (2009) https://doi.org/10.1117/12.812257

KEYWORDS: Esophagus, Cancer, Computer aided diagnosis and therapy, Medical imaging, Current controlled current source, Therapeutics, Standards development, Biopsy, Visible radiation

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SPIE Journal Paper | 1 July 2008

Segmentation and classification of anomalies in periodic structures

Yannick Caulier, Klaus Spinnler, Thomas Wittenberg, Salah Bourennane

JEI, Vol. 17, Issue 03, 033014, (July 2008) https://doi.org/10.1117/12.10.1117/1.2954127

KEYWORDS: Inspection, Image segmentation, 3D image processing, Image classification, Cameras, Sensors, Image processing, Reflectivity, Optical inspection, Light sources and illumination

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SPIE Journal Paper | 1 May 2008

Specific features for the analysis of fringe images

Yannick Caulier, Klaus Spinnler, Thomas Wittenberg, Salah Bourennane

OE, Vol. 47, Issue 05, 057201, (May 2008) https://doi.org/10.1117/12.10.1117/1.2927463

KEYWORDS: Fringe analysis, Inspection, Image classification, Image analysis, Light sources and illumination, Optical engineering, Nondestructive evaluation, Interferometry, Holographic interferometry, 3D image processing

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Proceedings Article | 11 March 2008 Paper

Paper

Boundary-precise segmentation of nucleus and plasma of leukocytes

Thorsten Zerfaß, Thomas Rehn, Thomas Wittenberg

Proceedings Volume 6914, 69143Q (2008) https://doi.org/10.1117/12.770370

KEYWORDS: Image segmentation, Blood, Plasma, Image processing algorithms and systems, LED lighting, RGB color model, Image processing, Visualization, Image filtering, Surgery

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Proceedings Article | 11 December 1996 Paper

Paper

High-speed imaging and image processing in voice disorders

Monika Tigges, Thomas Wittenberg, Frank Rosanowski, Ulrich Eysholdt

Proceedings Volume 2927, (1996) https://doi.org/10.1117/12.260642

KEYWORDS: Image processing, Cameras, Endoscopy, High speed cameras, Image resolution, Video, Optical resolution, High speed imaging, Digital recording, Acoustics

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Showing 5 of 16 publications

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