@article{BauerHamacherBroeeretal., author = {Bauer, Dagmar and Hamacher, Kurt and Br{\"o}er, Stefan and Pauleit, Dirk and Palm, Christoph and Zilles, Karl and Coenen, Heinz H. and Langen, Karl-Josef}, title = {Preferred stereoselective brain uptake of D-serine}, series = {Nuclear Medicine and Biology}, volume = {32}, journal = {Nuclear Medicine and Biology}, number = {8}, doi = {10.1016/j.nucmedbio.2005.07.004}, pages = {793 -- 797}, abstract = {Although it has long been presumed that d-amino acids are uncommon in mammalians, substantial amounts of free d-serine have been detected in the mammalian brain. d-Serine has been demonstrated to be an important modulator of glutamatergic neurotransmission and acts as an agonist at the strychnine-insensitive glycine site of N-methyl-d-aspartate receptors. The blood-to-brain transfer of d-serine is thought to be extremely low, and it is assumed that d-serine is generated by isomerization of l-serine in the brain. Stimulated by the observation of a preferred transport of the d-isomer of proline at the blood-brain barrier, we investigated the differential uptake of [3H]-d-serine and [3H]-l-serine in the rat brain 1 h after intravenous injection using quantitative autoradiography. Surprisingly, brain uptake of [3H]-d-serine was significantly higher than that of [3H]-l-serine, indicating a preferred transport of the d-enantiomer of serine at the blood-brain barrier. This finding indicates that exogenous d-serine may have a direct influence on glutamatergic neurotransmission and associated diseases.}, subject = {Aminos{\"a}uren}, language = {en} } @article{LehmannPalm, author = {Lehmann, Thomas M. and Palm, Christoph}, title = {Color Line Search for Illuminant Estimation in Real World Scenes}, series = {Journal of the Optical Society of America (JOSA) A}, volume = {18}, journal = {Journal of the Optical Society of America (JOSA) A}, number = {11}, doi = {10.1364/JOSAA.18.002679}, pages = {2679 -- 2691}, abstract = {The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [H. C. Lee, J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee's method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee's strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee's method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces.}, language = {en} } @article{Palm, author = {Palm, Christoph}, title = {Color Texture Classification by Integrative Co-Occurrence Matrices}, series = {Pattern Recognition}, volume = {37}, journal = {Pattern Recognition}, number = {5}, doi = {10.1016/j.patcog.2003.09.010}, pages = {965 -- 976}, abstract = {Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20\% and 32\% for the first and second baseline, respectively.}, language = {en} } @inproceedings{PalmVietenBaueretal., author = {Palm, Christoph and Vieten, Andrea and Bauer, Dagmar and Pietrzyk, Uwe}, title = {Evaluierung von Registrierungsstrategien zur multimodalen 3D-Rekonstruktion von Rattenhirnschnitten}, series = {Bildverarbeitung f{\"u}r die Medizin 2006}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2006}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/3-540-32137-3_51}, pages = {251 -- 255}, abstract = {In dieser Arbeit werden drei Strategien zur 3D Stapelung von multimodalen Schnittbildern vorgestellt. Die Strategien werden experimentell anhand von Dualtracer-Autoradiographien evaluiert. Dazu werden neue Maße zur Beschreibung der Konsistenz innerhalb einer Modalit{\"a}t und der Konsistenz der Modalit{\"a}ten untereinander entwickelt, die auf bekannten Registrierungsmetriken basieren. Gerade bez{\"u}glich der Konsistenz der Modalit{\"a}ten untereinander zeigen zwei Strategien die besten Resultate: (1) abwechselnde multimodale Registrierung (2) monomodale Rekonstruktion einer Modalit{\"a}t und multimodale 2D Registrierung der zweiten Modalit{\"a}t.}, subject = {Registrierung }, language = {de} } @inproceedings{PietrzykPalmBeyer, author = {Pietrzyk, Uwe and Palm, Christoph and Beyer, Thomas}, title = {Fusion strategies in multi-modality imaging}, series = {Medical Physics, Vol 2. Proceedings of the jointly held Congresses: ICMP 2005, 14th International Conference of Medical Physics of the International Organization for Medical Physics (IOMP), the European Federation of Organizations in Medical Physics (EFOMP) and the German Society of Medical Physics (DGMP) ; BMT 2005, 39th Annual Congress of the German Society for Biomedical Engineering (DGBMT) within VDE ; 14th - 17th September 2005, Nuremberg, Germany}, booktitle = {Medical Physics, Vol 2. Proceedings of the jointly held Congresses: ICMP 2005, 14th International Conference of Medical Physics of the International Organization for Medical Physics (IOMP), the European Federation of Organizations in Medical Physics (EFOMP) and the German Society of Medical Physics (DGMP) ; BMT 2005, 39th Annual Congress of the German Society for Biomedical Engineering (DGBMT) within VDE ; 14th - 17th September 2005, Nuremberg, Germany}, pages = {1446 -- 1447}, subject = {Bildgebendes Verfahren}, language = {en} } @misc{WeigertPalmQuicketal., author = {Weigert, Markus and Palm, Christoph and Quick, Harald H. and M{\"u}ller, Stefan P. and Pietrzyk, Uwe and Beyer, Thomas}, title = {Template for MR-based attenuation correction for whole-body PET/MR imaging}, series = {Nuklearmedizin}, volume = {46}, journal = {Nuklearmedizin}, number = {2}, pages = {A115}, subject = {Kernspintomografie}, language = {en} } @misc{AxerAxerPalmetal., author = {Axer, Markus and Axer, Hubertus and Palm, Christoph and Gr{\"a}ßel, David and Zilles, Karl and Pietrzyk, Uwe}, title = {Visualization of Nerve Fibre Orientation in the Visual Cortex of the Human Brain by Means of Polarized Light}, series = {Biomedizinische Technik}, volume = {52}, journal = {Biomedizinische Technik}, number = {Suppl.}, pages = {1569048-041}, subject = {Sehrinde}, language = {en} } @inproceedings{PalmDehnhardtVietenetal., author = {Palm, Christoph and Dehnhardt, Markus and Vieten, Andrea and Pietrzyk, Uwe}, title = {3D rat brain tumor reconstruction}, series = {Biomedizinische Technik}, volume = {50}, booktitle = {Biomedizinische Technik}, number = {Suppl. 1, Part 1}, pages = {597 -- 598}, subject = {Dreidimensionale Rekonstruktion}, language = {en} } @misc{PalmCrumPietrzyketal., author = {Palm, Christoph and Crum, William R. and Pietrzyk, Uwe and Hawkes, David J.}, title = {Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections}, series = {Biomedizinische Technik}, volume = {52}, journal = {Biomedizinische Technik}, number = {Suppl.}, pages = {1569048-859}, subject = {Registrierung }, language = {en} } @article{BrownConsortiumZhouetal., author = {Brown, Peter and Consortium, RELISH and Zhou, Yaoqi and Palm, Christoph}, title = {Large expert-curated database for benchmarking document similarity detection in biomedical literature search}, series = {Database}, volume = {2019}, journal = {Database}, publisher = {Oxford University Pres}, doi = {10.1093/database/baz085}, pages = {1 -- 66}, abstract = {Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76\% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.}, subject = {Information Retrieval}, language = {en} } @misc{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task}, series = {Endoscopy}, volume = {51}, journal = {Endoscopy}, number = {04}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0039-1681187}, pages = {6}, abstract = {Aims: The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task). Methods: An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D). Results: The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed. Conclusions: AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{PalmLehmannBrednoetal., author = {Palm, Christoph and Lehmann, Thomas M. and Bredno, J. and Neuschaefer-Rube, C. and Klajman, S. and Spitzer, Klaus}, title = {Automated Analysis of Stroboscopic Image Sequences by Vibration Profiles}, series = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 5th International Workshop}, booktitle = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 5th International Workshop}, abstract = {A method for automated segmentation of vocal cords in stroboscopic video sequences is presented. In contrast to earlier approaches, the inner and outer contours of the vocal cords are independently delineated. Automatic segmentation of the low contrasted images is carried out by connecting the shape constraint of a point distribution model to a multi-channel regionbased balloon model. This enables us to robustly compute a vibration profile that is used as a new diagnostic tool to visualize several vibration parameters in only one graphic. The vibration profiles are studied in two cases: one physiological vibration and one functional pathology.}, language = {en} } @inproceedings{PalmFischerLehmannetal., author = {Palm, Christoph and Fischer, B. and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Hierarchische Wasserscheiden-Transformation zur Lippensegmentierung in Farbbildern}, series = {Bildverarbeitung f{\"u}r die Medizin 2000}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2000}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-59757-2_20}, pages = {106 -- 110}, abstract = {Zur L{\"o}sung komplexer Segmentierungsprobleme wird eine hierarchische und farbbasierte Wasserscheidentransformation vorgestellt. Geringe Modifikationen bez{\"u}glich Startpunktwahl und Flutungsprozess resultieren in signifikanten Verbesserungen der Segmentierung. Das Verfahren wurde zur Lippendetektion in Farbbildsequenzen eingesetzt, die zur quantitativen Beschreibung von Sprechbewegungsabl{\"a}ufen automatisch ausgewertet werden. Die Experimente mit 245 Bildern aus 6 Sequenzen zeigten eine Fehlerrate von 13\%.}, language = {de} } @inproceedings{PalmLehmannSpitzer, author = {Palm, Christoph and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Color Texture Analysis of Moving Vocal Cords Using Approaches from Statistics and Signal Theory}, series = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 4th International Workshop, Friedrich Schiller University, Jena}, booktitle = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 4th International Workshop, Friedrich Schiller University, Jena}, pages = {49 -- 56}, abstract = {Textural features are applied for detection of morphological pathologies of vocal cords. Cooccurrence matrices as statistical features are presented as well as filter bank analysis by Gabor filters. Both methods are extended to handle color images. Their robustness against camera movement and vibration of vocal cords is evaluated. Classification results due to three in vivo sequences are in between 94.4 \% and 98.9\%. The classification errors decrease if color features are used instead of grayscale features for both statistical and Fourier features}, language = {en} } @article{NeuschaeferRubeLehmannPalmetal., author = {Neuschaefer-Rube, C. and Lehmann, Thomas M. and Palm, Christoph and Bredno, J. and Klajman, S. and Spitzer, Klaus}, title = {3D-Visualisierung glottaler Abduktionsbewegungen}, series = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, volume = {2001/2002}, journal = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, number = {9}, publisher = {Median}, isbn = {3-922766-76-5}, pages = {58 -- 61}, language = {de} } @inproceedings{FischerPalmLehmannetal., author = {Fischer, B. and Palm, Christoph and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Selektion von Farbtexturmerkmalen zur Tumorklassifikation dermatoskopischer Fotografien}, series = {Bildverarbeitung f{\"u}r die Medizin 2002}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2002}, publisher = {Springer}, address = {Berlin}, pages = {238 -- 241}, language = {de} } @book{Palm, author = {Palm, Christoph}, title = {Integrative Auswertung von Farbe und Textur}, publisher = {Der Andere Verlag}, language = {de} } @article{PalmDehnhardtVietenetal., author = {Palm, Christoph and Dehnhardt, Markus and Vieten, Andrea and Pietrzyk, Uwe and Bauer, Andreas and Zilles, Karl}, title = {3D rat brain tumors}, series = {Naunyn-Schmiedebergs Archives of Pharmacology}, volume = {371}, journal = {Naunyn-Schmiedebergs Archives of Pharmacology}, number = {R103}, language = {en} } @inproceedings{PalmSchollLehmannetal., author = {Palm, Christoph and Scholl, Ingrid and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Nutzung eines Farbkonstanz-Algorithmus zur Entfernung von Glanzlichtern in laryngoskopischen Bildern}, series = {Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft}, booktitle = {Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft}, editor = {Greiser, E. and Wischnewsky, M.}, publisher = {MMV Medien und Medizin}, address = {M{\"u}nchen}, isbn = {9783820813357}, pages = {300 -- 303}, abstract = {1 Einf{\"u}hrung Funktionelle und organische St{\"o}rungen im Larynx beeintr{\"a}chtigen die Ausdrucksf{\"a}higkeit des Menschen. Zur Diagnostik und Verlaufkontrolle werden die Stimmlippen im Larynx mit Hilfe der Video-Laryngoskopie aufgenommen. Zur optimalen Farbmessung wird dazu an das Lupenendoskop eine 3-Chip-CCD-Kamera angeschlossen, die eine unabh{\"a}ngige Aufnahme der drei Farbkan{\"a}le erlaubt. Die bisherige subjektive Befundung ist von der Erfahrung des Untersuchers abh{\"a}ngig und l{\"a}ßt nur eine grobe Klassifikation der Krankheitsbilder zu. Zur Objektivierung werden daher quantitative Parameter f{\"u}r Farbe, Textur und Schwingung entwickelt. Neben dem Einfluß der wechselnden Lichtquellenfarbe auf den Farbeindruck ist die Sekretauflage auf den Stimmlippen ein Problem bei der Farb-und Texturanalyse. Sie kann zu ausgedehnten Glanzlichtern f{\"u}hren und so weite Bereiche der Stimmlippen f{\"u}r die Farb-und Texturanalyse unbrauchbar machen. Dieser Beitrag stellt einen Farbkonstanz-Algorithmus vor, der unabh{\"a}ngig von der Lichtquelle quantitative Farbwerte des Gewebes liefert und die Glanzlichtdetektion und -elimination erm{\"o}glicht. 2 Methodik Ziel des Farbkonstanz-Algorithmus ist die Trennung von Lichtquellen-und Gewebefarbe. Unter Verwendung des dichromatischen Reflexionsmodells [1] kann die Oberfl{\"a}chenreflexion mit der Farbe der Lichtquelle und die K{\"o}rperreflexion mit der Gewebefarbe identifiziert werden. Der Farbeindruck entsteht aus der Linearkombination beider Farbkomponenten. Ihre Gewichtung ist von der Aufnahmegeometrie abh{\"a}ngig, insbesondere vom Winkel zwischen Oberfl{\"a}chennormalen und dem Positionsvektor der Lichtquelle. In einem zweistufigen Verfahren wird zun{\"a}chst die Lichtquellenfarbe gesch{\"a}tzt, dann die Gewebefarbe ermittelt. Hieraus k{\"o}nnen beide Farbanteile durch die Berechnung der Gewichtsfaktoren pixelweise getrennt werden.}, language = {de} } @inproceedings{ChangLinLeeetal., author = {Chang, Ching-Sheng and Lin, Jin-Fa and Lee, Ming-Ching and Palm, Christoph}, title = {Semantic Lung Segmentation Using Convolutional Neural Networks}, series = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, editor = {Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {10.1007/978-3-658-29267-6_17}, pages = {75 -- 80}, abstract = {Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today's medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9\% mIoU and 92\% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{SouzaJrEbigboProbstetal., author = {Souza Jr., Luis Antonio de and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Mendel, Robert and Palm, Christoph}, title = {Barrett's Esophagus Identification Using Color Co-occurrence Matrices}, series = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, booktitle = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, doi = {10.1109/SIBGRAPI.2018.00028}, pages = {166 -- 173}, abstract = {In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett'sEsophagus (BE)and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73\% for positive adenocarcinoma identification and specificity close to 77\% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67\% and specificity around to76\%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.}, language = {en} } @inproceedings{MiddelPalmErdt, author = {Middel, Luise and Palm, Christoph and Erdt, Marius}, title = {Synthesis of Medical Images Using GANs}, series = {Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019}, booktitle = {Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019}, publisher = {Springer Nature}, address = {Cham}, isbn = {978-3-030-32688-3}, issn = {0302-9743}, doi = {10.1007/978-3-030-32689-0_13}, pages = {125 -- 134}, abstract = {The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.}, subject = {Neuronale Netze}, language = {en} } @inproceedings{PalmLehmannSpitzer, author = {Palm, Christoph and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Bestimmung der Lichtquellenfarbe bei der Endoskopie mikrotexturierter Oberfl{\"a}chen des Kehlkopfes}, series = {5. Workshop Farbbildverarbeitung, Ilmenau, 1999}, booktitle = {5. Workshop Farbbildverarbeitung, Ilmenau, 1999}, pages = {3 -- 10}, abstract = {Zur Unterst{\"u}tzung der Diagnose von Stimmlippenerkrankungen werden innerhalb des Forschungsprojektes Quantitative Digitale Laryngoskopie objektive Parameter zur Beschreibung der Bewegung, der Farbe sowie der Form der Stimmlippen entwickelt und klinisch evaluiert. W{\"a}hrend die Bewegungsanalyse Aufschluß {\"u}ber funktionelle Stimmst{\"o}rungen gibt, beschreiben Parameter der Farb- und Formanalyse morphologische Ver{\"a}nderungen des Stimmlippengewebes. In diesem Beitrag werden die Methoden und bisherigen Ergebnisse zur Bewegungs- und Farbanalyse vorgestellt. Die Bewegungsanalyse wurde mit einem erweiterten Konturmodell (Snakes) durchgef{\"u}hrt. Aufgrund des modifizierten Konturmodells konnten die Konturen der Stimmlippen automatisch {\"u}ber die gesmate Bildsequenz zuverl{\"a}ssig detektiert werden. Die Vermssung der Konturen liefert neue quantitative Parameter zur Befundung von laryngoskopischen Stimmlippenaufnahmen. Um die Farbeigenschaften der Stimmlippen zu bestimmen, wurde ausgehend vom RGB-Bild die Objektfarbe unabh{\"a}ngig von der Farbe der Lichtquelle durch Verwendung von Clusterverfahren und der Viertelkreisanalyse berechnet. Mit dieser Farbanalyse konnte die Farbe der Lichtquelle ermittelt und das beleuchtungsunabh{\"a}ngige Farbbild berechnet werden. Die Quanitifizierung der R{\"o}tung der Stimmlippen ist z.B. ein entscheidendes Kriterium zur Diagnostik der akuten Laryngitis.}, language = {de} } @inproceedings{WeihererZornWittenbergetal., author = {Weiherer, Maximilian and Zorn, Martin and Wittenberg, Thomas and Palm, Christoph}, title = {Retrospective Color Shading Correction for Endoscopic Images}, series = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, editor = {Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {10.1007/978-3-658-29267-6}, pages = {14 -- 19}, abstract = {In this paper, we address the problem of retrospective color shading correction. An extension of the established gray-level shading correction algorithm based on signal envelope (SE) estimation to color images is developed using principal color components. Compared to the probably most general shading correction algorithm based on entropy minimization, SE estimation does not need any computationally expensive optimization and thus can be implemented more effciently. We tested our new shading correction scheme on artificial as well as real endoscopic images and observed promising results. Additionally, an indepth analysis of the stop criterion used in the SE estimation algorithm is provided leading to the conclusion that a fixed, user-defined threshold is generally not feasible. Thus, we present new ideas how to develop a non-parametric version of the SE estimation algorithm using entropy.}, subject = {Endoskopie}, language = {en} }