Prof. Henning Müller Profile

Prof. Henning Müller

at HES-SO Valais-Wallis

SPIE Involvement:

Author

Publications (43)

Proceedings Article | 3 April 2024 Poster + Paper

Poster + Paper

Automated classification of celiac disease in histopathological images: a multi-scale approach

Simon Püttmann, Lluis Borras Ferris, Niccoló Marini, Witali Aswolinsky, Simona Vatrano, Filippo Fragetta, Iris Nagtegaal, Chella van der Post, Francesco Ciompi, Manfredo Atzori, Christoph Friedrich, Henning Müller

Proceedings Volume 12927, 129272D (2024) https://doi.org/10.1117/12.3006669

KEYWORDS: Data modeling, Tissues, Pathology, Diseases and disorders, Feature extraction, Biopsy, Image classification, Education and training, Performance modeling, Histopathology

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Proceedings Article | 3 April 2024 Presentation + Paper

Presentation + Paper

A full pipeline to analyze lung histopathology images

Lluis Borràs Ferrís, Simon Püttmann, Niccoló Marini, Simona Vatrano, Filippo Fragetta, Alessandro Caputo, Francesco Ciompi, Manfredo Atzori, Henning Müller

Proceedings Volume 12933, 1293303 (2024) https://doi.org/10.1117/12.3006708

KEYWORDS: Tumor growth modeling, Cancer, Data modeling, Tissues, Lung, Machine learning, Lung cancer, Education and training, Performance modeling, Histopathology

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Proceedings Article | 2 April 2024 Presentation + Paper

Presentation + Paper

A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography

Anastasiia Rozhyna, Manfredo Atzori, Henning Müller

Proceedings Volume 12931, 129310R (2024) https://doi.org/10.1117/12.3006872

KEYWORDS: Deep convolutional neural networks, Retinal diseases, Eye, Deep learning, Optical coherence tomography, Machine learning, Image classification

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Proceedings Article | 29 March 2024 Poster + Paper

Poster + Paper

Structured radiology report text analysis using natural language processing for automatic billing

Nicolas Devanthery, Natalie Heracleous, Benoit Dufour, Jean-Daniel Fardel, Benoit Rizk, Hugues Brat, Cyril Thouly, Henning Müller, Federica Zanca, Lluis Borràs Ferrís

Proceedings Volume 12929, 129290Y (2024) https://doi.org/10.1117/12.3005518

KEYWORDS: Data mining, Data modeling, Quality control, Doppler effect, Tissues, Ultrasonography, Abdomen, Medical imaging, Radiology, Algorithm development

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Purpose: The aim of this study was to develop an algorithm for automated quality control of structured radiology reports and to automatically obtain the correct invoicing codes for the performed exam. Ultrasound (US) exams of the abdomen were selected as use case, including Doppler exams. Method: To build a correct algorithm for automatic billing, the billing tree for the Ultrasound exams was studied. In Switzerland, TARMED, is the tariff structure used for billing outpatient medical services. The 4600 services listed in TARMED are divided into chapters that group together all services with a well-defined common characteristic. For example, Chapter 39 covers all medical imaging services. These chapters are further subdivided into subchapters for greater precision. Using this information a modular Natural Language Processing algorithm based on the Natural Language Toolkit (NLTK) library was developed. A second NLP algorithm based on SPACY was also developed, with the objective of a double validation of the first developed NLP algorithm. To train and test the algorithm a dataset of 170 exams corresponding to US abdominal examinations along with their radiology report were extracted from our RIS. The results of the algorithm were validated by an experienced technologists which identified possible discrepancy between the algorithm results and the correct billing. This check was carried out on a batch of data containing 95 samples. A confusion matrix was used to analyze the results. Results: In all 95 data samples, the NLTK algorithm was able to detect the billing codes correctly 100% of the time. In all our 95 data samples, the Spacy algorithm was able to detect the billing codes correctly in 86.3% of cases. This algorithm tends to overestimate the type of abdominal examination present in the report. Indeed, the 13 cases in which the algorithm made an error were cases where it detected a full abdominal ultrasound when the examination was a simple lower or upper abdomen. Conclusion: The NLTK model provides reliable and efficient estimation of billing codes for abdominal ultrasound, facilitating the task of the technologies who saves time and avoids possible human errors.

Proceedings Article | 16 March 2020 Presentation + Paper

Presentation + Paper

Systematic comparison of deep learning strategies for weakly supervised Gleason grading

Sebastian Otálora, Manfredo Atzori, Amjad Khan, Oscar Jimenez-del-Toro, Vincent Andrearczyk, Henning Müller

Proceedings Volume 11320, 113200L (2020) https://doi.org/10.1117/12.2548571

KEYWORDS: Principal component analysis, Cancer, Data modeling, Tissues, Pathology, Feature extraction, Biopsy, Machine learning, Prostate, Performance modeling

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Proceedings Article | 16 March 2020 Presentation + Paper

Presentation + Paper

Generalizing convolution neural networks on stain color heterogeneous data for computational pathology

Amjad Khan, Manfredo Atzori, Sebastian Otálora, Vincent Andrearczyk, Henning Müller

Proceedings Volume 11320, 113200R (2020) https://doi.org/10.1117/12.2549718

KEYWORDS: Tumors, Visualization, Tissues, Scanners, Pathology, Data acquisition, Neural networks, Convolution, Data centers, RGB color model

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Proceedings Article | 10 March 2020 Presentation + Paper

Presentation + Paper

An exploration of uncertainty information for segmentation quality assessment

Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay Patel, Ken Chang, Adrien Depeursinge, Henning Müller, Jayashree Kalpathy-Cramer

Proceedings Volume 11313, 113131K (2020) https://doi.org/10.1117/12.2548722

KEYWORDS: Data modeling, Visualization, Image segmentation, Lung, Medical imaging, Statistical modeling

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Proceedings Article | 2 March 2020 Presentation + Paper

Presentation + Paper

Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies

Anjani Dhrangadhariya, Oscar Jimenez-del-Toro, Vincent Andrearczyk, Manfredo Atzori, Henning Müller

Proceedings Volume 11318, 113180A (2020) https://doi.org/10.1117/12.2549565

KEYWORDS: Biomedical optics, Tumor growth modeling, Cancer, Data modeling, Visualization, Microscopy, Mining, Machine learning, Image classification, Lawrencium

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SPIE Journal Paper | 6 June 2019

Neural network training for cross-protocol radiomic feature standardization in computed tomography

Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

JMI, Vol. 6, Issue 02, 024008, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024008

KEYWORDS: Scanners, Feature extraction, Principal component analysis, Neural networks, Computed tomography, Image classification, Medical imaging, Reconstruction algorithms, Tissues, Image acquisition

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Proceedings Article | 15 March 2019 Presentation + Paper

Presentation + Paper

Learning cross-protocol radiomics and deep feature standardization from CT images of texture phantoms

Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

Proceedings Volume 10954, 109540I (2019) https://doi.org/10.1117/12.2512683

KEYWORDS: Principal component analysis, Tissues, Image processing, Scanners, Image acquisition, Feature extraction, Medical imaging, Neural networks, Computed tomography, Lung cancer

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Proceedings Article | 13 March 2019 Presentation + Paper

Presentation + Paper

Improved interpretability for computer-aided severity assessment of retinopathy of prematurity

Mara Graziani, James Brown, Vincent Andrearczyk, Veysi Yildiz, J. Peter Campbell, Deniz Erdogmus, Stratis Ioannidis, Michael Chiang, Jayashree Kalpathy-Cramer, Henning Müller

Proceedings Volume 10950, 109501R (2019) https://doi.org/10.1117/12.2512584

KEYWORDS: Visual process modeling, Computer aided diagnosis and therapy, Convolutional neural networks, Visualization, Image segmentation, Diagnostics, Feature extraction, Machine learning, Network architectures, Neurons

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Proceedings Article | 6 March 2018 Paper

Paper

Tumor proliferation assessment of whole slide images

Mikael Rousson, Martin Hedlund, Mats Andersson, Ludwig Jacobsson, Gunnar Lathen, Bjorn Norell, Oscar Jimenez-del-Toro, Henning Mueller, Manfredo Atzori

Proceedings Volume 10581, 105810Y (2018) https://doi.org/10.1117/12.2293634

KEYWORDS: Breast cancer, Detection and tracking algorithms, Data modeling, Image segmentation, Pathology, Image classification, Algorithm development, Network architectures

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Proceedings Article | 27 February 2018 Paper

Paper

An automatically generated texture-based atlas of the lungs

Yashin Dicente Cid, Oula Puonti, Alexandra Platon, Koen Van Leemput, Henning Müller, Pierre-Alexandre Poletti

Proceedings Volume 10575, 105753A (2018) https://doi.org/10.1117/12.2294004

KEYWORDS: Chest, Visualization, Databases, Image segmentation, Lung, Spatial analysis, Computed tomography, Pulmonary hypertension

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Proceedings Article | 13 March 2017 Presentation + Paper

Presentation + Paper

Making sense of large data sets without annotations: analyzing age-related correlations from lung CT scans

Yashin Dicente Cid, Artem Mamonov, Andrew Beers, Armin Thomas, Vassili Kovalev, Jayashree Kalpathy-Cramer, Henning Müller

Proceedings Volume 10138, 1013809 (2017) https://doi.org/10.1117/12.2255609

KEYWORDS: Data modeling, Visualization, Tissues, Databases, Image segmentation, Feature extraction, Lung, Medical imaging, Computed tomography, Data analysis

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The analysis of large data sets can help to gain knowledge about specific organs or on specific diseases, just as big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so the visual content of lung CT scans of a large number of patients. In the case of the described data set, only little annotation is available on the patients that were all part of an ongoing screening program and besides age and gender no information on the patient and the findings was available for this work. This is a scenario that can happen regularly as image data sets are produced and become available in increasingly large quantities but manual annotations are often not available and also clinical data such as text reports are often harder to share. We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken in the context of a national lung screening program in Belarus. Lung fields were segmented by two segmentation algorithms and only cases where both algorithms were able to find left and right lung and had a Dice coefficient above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65 years where a steady trend is seen. For young adults and older people the results are not as good as variability is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture, size and density with age. The experiments allow learning the evolution of the lung and the gained results show that even with limited metadata we can extract interesting information from large-scale visual data. These age-related changes (for example of the lung volume, the density histogram of the tissue) can also be taken into account for the interpretation of new cases. The database used includes patients that had suspicions on a chest X-ray, so it is not a group of healthy people, and only tendencies and not a model of a healthy lung at a specific age can be derived.

Proceedings Article | 1 March 2017 Paper

Paper

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

Oscar Jiménez del Toro, Manfredo Atzori, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Peter Rönnquist, Henning Müller

Proceedings Volume 10140, 101400O (2017) https://doi.org/10.1117/12.2255710

KEYWORDS: Cancer, Convolutional neural networks, Data modeling, Tumors, Visualization, Tissues, Image segmentation, Pathology, Image analysis, Neural networks, Prostate, Image classification, Prostate cancer, Information visualization, RGB color model

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The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high–grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions–of–interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2–class decision: high vs. low Gleason grade. Grades 7–8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re–training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically generated heatmaps for theWSIs could be useful for improving the selection of patches when training networks for big data sets and to guide the visual inspection of these images.

Proceedings Article | 20 March 2015 Paper

Paper

Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations

Ranveer Joyseeree, Henning Müller

Proceedings Volume 9413, 94133S (2015) https://doi.org/10.1117/12.2081204

KEYWORDS: Liver, Detection and tracking algorithms, Magnetic resonance imaging, Image segmentation, Image registration, Lung, Abdomen, Kidney, Computed tomography, 3D image processing

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Proceedings Article | 20 March 2015 Paper

Paper

Texture classification of anatomical structures in CT using a context-free machine learning approach

Oscar Jiménez del Toro, Antonio Foncubierta-Rodríguez, Adrien Depeursinge, Henning Müller

Proceedings Volume 9414, 94140W (2015) https://doi.org/10.1117/12.2082273

KEYWORDS: Liver, Visual analytics, Visualization, Image segmentation, Matrices, Lung, Spleen, Medical imaging, Bladder, Information visualization

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Proceedings Article | 20 March 2015 Paper

Paper

Pulmonary embolism detection using localized vessel-based features in dual energy CT

Yashin Dicente Cid, Adrien Depeursinge, Antonio Foncubierta Rodríguez, Alexandra Platon, Pierre-Alexandre Poletti, Henning Müller

Proceedings Volume 9414, 941407 (2015) https://doi.org/10.1117/12.2082791

KEYWORDS: Prisms, Computer aided diagnosis and therapy, Visualization, Signal attenuation, Image segmentation, Lung, Iodine, Computed tomography, Performance modeling, Dual energy imaging

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Proceedings Article | 17 March 2015 Paper

Paper

Medical case-based retrieval: integrating query MeSH terms for query-adaptive multi-modal fusion

Alba Seco de Herrera, Antonio Foncubierta-Rodríguez, Henning Müller

Proceedings Volume 9418, 94180S (2015) https://doi.org/10.1117/12.2082028

KEYWORDS: Image fusion, Medicine, Biomedical optics, Visualization, Matrices, Feature extraction, Medical imaging, Neodymium, Binary data, Information visualization

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Proceedings Article | 21 March 2014 Paper

Paper

Finding seed points for organ segmentation using example annotations

Ranveer Joyseeree, Henning Müller

Proceedings Volume 9034, 903444 (2014) https://doi.org/10.1117/12.2043037

KEYWORDS: Image processing algorithms and systems, Liver, 3D acquisition, Detection and tracking algorithms, Tissues, Magnetic resonance imaging, Image segmentation, Lung, Computed tomography, 3D image processing

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Proceedings Article | 26 September 2013 Paper

Paper

Rotation-covariant visual concept detection using steerable Riesz wavelets and bags of visual words

Adrien Depeursinge, Antonio Foncubierta, Henning Müller, Dimitri Van de Ville

Proceedings Volume 8858, 885816 (2013) https://doi.org/10.1117/12.2023806

KEYWORDS: Image visualization, Radon, Principal component analysis, Visual process modeling, Visual analytics, Data modeling, Visualization, Sensors, Image processing, Wavelets

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Proceedings Article | 29 March 2013 Paper

Paper

Region-based volumetric medical image retrieval

Antonio Foncubierta-Rodríguez, Henning Müller, Adrien Depeursinge

Proceedings Volume 8674, 867406 (2013) https://doi.org/10.1117/12.2007971

KEYWORDS: Human-machine interfaces, Visualization, Sensors, Wavelets, Image analysis, Lung, Medical imaging, Image retrieval, 3D image processing, Information visualization

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Proceedings Article | 29 March 2013 Paper

Paper

Determining the relative importance of figures in journal articles to find representative images

Henning Müller, Antonio Foncubierta-Rodríguez, Chang Lin, Ivan Eggel

Proceedings Volume 8674, 86740I (2013) https://doi.org/10.1117/12.2007843

KEYWORDS: Image visualization, Cell phones, Biomedical optics, Visualization, Image processing, Diagnostics, Medical imaging, Tablets, Image retrieval, Algorithm development

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When physicians are searching for articles in the medical literature, images of the articles can help determining relevance of the article content for a specific information need. The visual image representation can be an advantage in effectiveness (quality of found articles) and also in efficiency (speed of determining relevance or irrelevance) as many articles can likely be excluded much quicker by looking at a few representative images. In domains such as medical information retrieval, allowing to determine relevance quickly and accurately is an important criterion. This becomes even more important when small interfaces are used as it is frequently the case on mobile phones and tablets to access scientific data whenever information needs arise. In scientific articles many figures are used and particularly in the biomedical literature only a subset may be relevant for determining the relevance of a specific article to an information need. In many cases clinical images can be seen as more important for visual appearance than graphs or histograms that require looking at the context for interpretation. To get a clearer idea of image relevance in articles, a user test with a physician was performed who classified images of biomedical research articles into categories of importance that can subsequently be used to evaluate algorithms that automatically select images as representative examples. The manual sorting of images of 50 journal articles of BioMedCentral with each containing more than 8 figures by importance also allows to derive several rules that determine how to choose images and how to develop algorithms for choosing the most representative images of specific texts. This article describes the user tests and can be a first important step to evaluate automatic tools to select representative images for representing articles and potentially also images in other contexts, for example when representing patient records or other medical concepts when selecting images to represent RadLex terms in tutorials or interactive interfaces for example. This can help to make the image retrieval process more efficient and effective for physicians.

Proceedings Article | 29 March 2013 Paper

Paper

Separating compound figures in journal articles to allow for subfigure classification

Ajad Chhatkuli, Antonio Foncubierta-Rodríguez, Dimitrios Markonis, Fabrice Meriaudeau, Henning Müller

Proceedings Volume 8674, 86740J (2013) https://doi.org/10.1117/12.2007897

KEYWORDS: Biomedical optics, Visualization, Databases, Image segmentation, Image analysis, Medical imaging, Image filtering, Image retrieval, Image classification, Algorithm development

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Proceedings Article | 16 February 2012 Paper

Paper

Synchronized slice viewing of similar image series

Sharib Ali, Antonio Foncubierta, Adrien Depeursinge, Fabrice Meriaudeau, Osman Ratib, Henning Müller

Proceedings Volume 8319, 83190K (2012) https://doi.org/10.1117/12.911233

KEYWORDS: Visualization, Magnetic resonance imaging, Image restoration, Image analysis, Image registration, Lung, Computed tomography, Neuroimaging, Cerebellum, Brain

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Comparing several series of images is not always easy as the corresponding slices often need to be selected manually. In times where series contain an ever-increasing number of slices this can mean manual work when moving several series to the corresponding slice. Particularly two situations were identified in this context: (1) patients with a large number of image series over time (such as patients with cancers that are monitored) frequently need to compare the series, for example to compare tumor growth over time. Manually adapting two series is possible but with four or more series this can mean loosing time. Having automatically the closest slice by comparing visual similarity also in older series with differing slice thickness and inter slice distance can save time and synchronize the viewing instantly. (2) analyzing visually similar image series of several patients can profit from being viewed in a synchronized way to compare the cases, so when sliding through the slices in one volume, the corresponding slices in the other volumes are shown. This application could be employed after content-based 3D image retrieval has found similar series, for example. Synchronized viewing can help finding or confirming the most relevant cases quickly. To allow for synchronized viewing of several image volumes, the test image series are first registered applying affine transformation for the global registration of images followed by diffeomorphic image registration. Then corresponding slices in the two volumes are estimated based on a visual similarity. Once the registration is finished, the user can subsequently move inside the slices of one volume (reference volume) and can view the corresponding slices in the other volumes. These corresponding slices are obtained after a correspondence match in the registration procedure. These volumes are synchronized in that the slice closest to the original reference volume is shown even when the slice thicknesses or inter slice distances differ, and this is automatically done by comparing the visual image content of the slices. The tool has the potential to help in a variety of situations and it is currently being made available as a plugin for the popular Osirix image viewer.

Proceedings Article | 16 February 2012 Paper

Paper

Creating a classification of image types in the medical literature for visual categorization

Henning Müller, Jayashree Kalpathy-Cramer, Dina Demner-Fushman, Sameer Antani

Proceedings Volume 8319, 83190P (2012) https://doi.org/10.1117/12.911186

KEYWORDS: Biomedical optics, Image compression, 3D image reconstruction, Visualization, Microscopy, Medical imaging, Computed tomography, Image retrieval, Image classification, Radiology

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Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

Proceedings Article | 16 February 2012 Paper

Paper

Multi-scale visual words for hierarchical medical image categorisation

Dimitrios Markonis, Alba Seco de Herrera, Ivan Eggel, Henning Müller

Proceedings Volume 8319, 83190F (2012) https://doi.org/10.1117/12.911550

KEYWORDS: Biomedical optics, Visualization, Magnetic resonance imaging, Medical imaging, Computed tomography, Image retrieval, Image classification, Radiology, Information visualization, Web services

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Proceedings Article | 14 February 2012 Paper

Paper

Three dimensional multi-scale visual words for texture-based cerebellum segmentation

Antonio Foncubierta-Rodríguez, Adrien Depeursinge, Laura Gui, Henning Müller

Proceedings Volume 8314, 83142Z (2012) https://doi.org/10.1117/12.911523

KEYWORDS: Visualization, Magnetic resonance imaging, Image segmentation, Wavelets, Image analysis, Medical imaging, Image retrieval, Neuroimaging, Cerebellum, Brain

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Proceedings Article | 25 March 2011 Paper

Paper

Mobile medical image retrieval

Samuel Duc, Adrien Depeursinge, Ivan Eggel, Henning Müller

Proceedings Volume 7967, 79670G (2011) https://doi.org/10.1117/12.877054

KEYWORDS: Human-machine interfaces, Visualization, Cameras, Databases, Feature extraction, Medical imaging, Image retrieval, Radiology, Operating systems, Information visualization

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Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in the text. Problems with the many, often incompatible mobile platforms were discovered and are listed in the text. Mobile information access is a quickly growing domain and the constraints of mobile access also need to be taken into account for image retrieval. The demonstrated access to the medical literature is most relevant as the medical literature and their images are clearly the largest knowledge source in the medical field.

Proceedings Article | 25 March 2011 Paper

Paper

Multiscale salient point-based retrieval of fracture cases

Xin Zhou, Richard Stern, Adrien Depeursinge, Henning Müller

Proceedings Volume 7967, 796706 (2011) https://doi.org/10.1117/12.878046

KEYWORDS: Visualization, Surgery, Sensors, Databases, Image processing, X-rays, Feature extraction, Bone, Performance modeling, Information visualization

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Proceedings Article | 9 March 2011 Paper

Paper

3D lung image retrieval using localized features

Adrien Depeursinge, Tatjana Zrimec, Sata Busayarat, Henning Müller

Proceedings Volume 7963, 79632E (2011) https://doi.org/10.1117/12.877943

KEYWORDS: Chest, Visualization, Tissues, Image segmentation, Glasses, Diagnostics, Feature extraction, Lung, Image retrieval, 3D image processing

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Proceedings Article | 13 March 2009 Paper

Paper

Comparing the quality of accessing medical literature using content-based visual and textual information retrieval

Henning Müller, Jayashree Kalpathy-Cramer, Charles Kahn, William Hersh

Proceedings Volume 7264, 726405 (2009) https://doi.org/10.1117/12.811416

KEYWORDS: Visualization, Databases, Medical research, Medical imaging, Image retrieval, Image classification, Radiology, X-ray imaging, Health informatics, Information visualization

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Content-based visual information (or image) retrieval (CBIR) has been an extremely active research domain within medical imaging over the past ten years, with the goal of improving the management of visual medical information. Many technical solutions have been proposed, and application scenarios for image retrieval as well as image classification have been set up. However, in contrast to medical information retrieval using textual methods, visual retrieval has only rarely been applied in clinical practice. This is despite the large amount and variety of visual information produced in hospitals every day. This information overload imposes a significant burden upon clinicians, and CBIR technologies have the potential to help the situation. However, in order for CBIR to become an accepted clinical tool, it must demonstrate a higher level of technical maturity than it has to date. Since 2004, the ImageCLEF benchmark has included a task for the comparison of visual information retrieval algorithms for medical applications. In 2005, a task for medical image classification was introduced and both tasks have been run successfully for the past four years. These benchmarks allow an annual comparison of visual retrieval techniques based on the same data sets and the same query tasks, enabling the meaningful comparison of various retrieval techniques. The datasets used from 2004-2007 contained images and annotations from medical teaching files. In 2008, however, the dataset used was made up of 67,000 images (along with their associated figure captions and the full text of their corresponding articles) from two Radiological Society of North America (RSNA) scientific journals. This article describes the results of the medical image retrieval task of the ImageCLEF 2008 evaluation campaign. We compare the retrieval results of both visual and textual information retrieval systems from 15 research groups on the aforementioned data set. The results show clearly that, currently, visual retrieval alone does not achieve the performance necessary for real-world clinical applications. Most of the common visual retrieval techniques have a MAP (Mean Average Precision) of around 2-3%, which is much lower than that achieved using textual retrieval (MAP=29%). Advanced machine learning techniques, together with good training data, have been shown to improve the performance of visual retrieval systems in the past. Multimodal retrieval (basing retrieval on both visual and textual information) can achieve better results than purely visual, but only when carefully applied. In many cases, multimodal retrieval systems performed even worse than purely textual retrieval systems. On the other hand, some multimodal retrieval systems demonstrated significantly increased early precision, which has been shown to be a desirable behavior in real-world systems.

Proceedings Article | 28 February 2009 Paper

Paper

Toward translational incremental similarity-based reasoning in breast cancer grading

Adina Tutac, Daniel Racoceanu, Wee-Keng Leow, Henning Müller, Thomas Putti, Vladimir Cretu

Proceedings Volume 7260, 72603C (2009) https://doi.org/10.1117/12.813731

KEYWORDS: Content based image retrieval, Computer aided diagnosis and therapy, Breast cancer, Visualization, Databases, Pathology, Medical research, Feature extraction, Artificial intelligence, Image retrieval

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Proceedings Article | 11 March 2008 Paper

Paper

A classification framework for lung tissue categorization

Adrien Depeursinge, Jimison Iavindrasana, Asmâa Hidki, Gilles Cohen, Antoine Geissbuhler, Alexandra Platon, Pierre-Alexandre Poletti, Henning Müller

Proceedings Volume 6919, 69190C (2008) https://doi.org/10.1117/12.769190

KEYWORDS: Computer aided diagnosis and therapy, Data modeling, Tissues, Wavelets, Computing systems, Feature extraction, Lung, Java, Machine learning, Image classification

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Proceedings Article | 31 March 2007 Paper

Paper

Image-based diagnostic aid for interstitial lung disease with secondary data integration

Adrien Depeursinge, Henning Müller, Asmâa Hidki, Pierre-Alexandre Poletti, Alexandra Platon, Antoine Geissbuhler

Proceedings Volume 6514, 65143P (2007) https://doi.org/10.1117/12.709533

KEYWORDS: Chest, Visualization, Tissues, Databases, Diagnostics, Lung, Medical imaging, Computed tomography, Image classification, Data integration

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Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

Proceedings Article | 21 March 2007 Paper

Paper

Content-based image retrieval from a database of fracture images

Henning Müller, Phuong Anh Do Hoang, Adrien Depeursinge, Pierre Hoffmeyer, Richard Stern, Christian Lovis, Antoine Geissbuhler

Proceedings Volume 6516, 65160H (2007) https://doi.org/10.1117/12.709516

KEYWORDS: Human-machine interfaces, Visualization, Surgery, Databases, Feature extraction, Medical imaging, Image filtering, Image retrieval, Prototyping, Classification systems

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This article describes the use of a medical image retrieval system on a database of 16'000 fractures, selected from surgical routine over several years. Image retrieval has been a very active domain of research for several years. It was frequently proposed for the medical domain, but only few running systems were ever tested in clinical routine. For the planning of surgical interventions after fractures, x-ray images play an important role. The fractures are classified according to exact fracture location, plus whether and to which degree the fracture is damaging articulations to see how complicated a reparation will be. Several classification systems for fractures exist and the classification plus the experience of the surgeon lead in the end to the choice of surgical technique (screw, metal plate, ...). This choice is strongly influenced by the experience and knowledge of the surgeons with respect to a certain technique. Goal of this article is to describe a prototype that supplies similar cases to an example to help treatment planning and find the most appropriate technique for a surgical intervention. Our database contains over 16'000 fracture images before and after a surgical intervention. We use an image retrieval system (GNU Image Finding Tool, GIFT) to find cases/images similar to an example case currently under observation. Problems encountered are varying illumination of images as well as strong anatomic differences between patients. Regions of interest are usually small and the retrieval system needs to focus on this region. Results show that GIFT is capable of supplying similar cases, particularly when using relevance feedback, on such a large database. Usual image retrieval is based on a single image as search target but for this application we have to select images by case as similar cases need to be found and not images. A few false positive cases often remain in the results but they can be sorted out quickly by the surgeons. Image retrieval can well be used for the planning of operations by supplying similar cases. A variety of challenges has been identified and partly solved (varying luminosity, small region of interested, case-based instead of image-based). This article mainly presents a case study to identify potential benefits and problems. Several steps for improving the system have been identified as well and will be described at the end of the paper.

Proceedings Article | 16 January 2006 Paper

Paper

Using heterogeneous annotation and visual information for the benchmarking of image retrieval system

Henning Müller, Paul Clough, William Hersh, Thomas Deselaers, Thomas Lehmann, Bruno Janvier, Antoine Geissbuhler

Proceedings Volume 6061, 606105 (2006) https://doi.org/10.1117/12.660259

KEYWORDS: Visualization, Databases, Pathology, Radiography, Photography, Feature extraction, Medical imaging, Image retrieval, Multimedia, Information visualization

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Proceedings Article | 15 April 2005 Paper

Paper

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

Wei Xiong, Bo Qiu, Qi Tian, Henning Mueller, Changsheng Xu

Proceedings Volume 5748, (2005) https://doi.org/10.1117/12.594857

KEYWORDS: Content based image retrieval, Image visualization, Visualization, Databases, Image segmentation, Image processing, Medical research, Feature extraction, Medical imaging, Image retrieval

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Proceedings Article | 19 April 2004 Paper

Paper

Comparing features sets for content-based image retrieval in a medical-case database

Henning Muller, Antoine Rosset, Jean-Paul Vallee, Antoine Geissbuhler

Proceedings Volume 5371, (2004) https://doi.org/10.1117/12.535039

KEYWORDS: Content based image retrieval, Databases, Computing systems, Diagnostics, Feature extraction, Medical imaging, Image quality, Quantization, Image retrieval, Picture Archiving and Communication System

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Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the texture analysis also leads to improved results but response time is going up equally due to the larger feature space. CBIRSs can be of great use in managing large medical image databases. They allow to find images that might otherwise be lost for research and publications. They also give students students the possibility to navigate within large image repositories. In the future, CBIR might also become more important in case-based reasoning and evidence-based medicine to support the diagnostics because first studies show good results.

Proceedings Article | 11 October 2000 Paper

Paper

MRML: an extensible communication protocol for interoperability and benchmarking of multimedia information retrieval systems

Wolfgang Mueller, Henning Mueller, Stephane Marchand-Maillet, Thierry Pun, David Squire, Zoran Pecenovic, Christoph Giess, Arjen de Vries

Proceedings Volume 4210, (2000) https://doi.org/10.1117/12.403795

KEYWORDS: Content based image retrieval, Human-machine interfaces, Databases, Telecommunications, Analytical research, Multimedia

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Proceedings Article | 11 October 2000 Paper

Paper

Toward a fair benchmark for image browsers

Wolfgang Mueller, Stephane Marchand-Maillet, Henning Mueller, Thierry Pun

Proceedings Volume 4210, (2000) https://doi.org/10.1117/12.403810

KEYWORDS: Monte Carlo methods, Distance measurement, Multimedia, Stochastic processes, Performance modeling, Systems modeling

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The recent literature has shown that the principal difficulty in multimedia retrieval is the bridging of the semantic gap between the user's wishes and his ability to formulate queries. This insight has spawned two main directions of research Query By Example (QBE) with relevance feedback (i.e. learning to improve the result of a previously formulated query) and the research in query formulation techniques, like browsing or query by sketch. Browsing techniques try to help the user in finding his target image, or an image which is sufficiently close to the desired result that it can be used in a subsequent QBE query. From the feature space viewpoint, each browsing system tries to permit the user to move consciously in feature space and eventually reach the target image. How to provide this functionality to the user is presently an open question. In fact even obtaining objective performance evaluation and comparison of these browsing paradigms is difficult. We distinguish between deterministic browsers, which try to optimize the possibility for the user to learn how the system behaves, and stochastic browsers based on more sophisticated Monte-Carlo algorithms thus sacrificing reproducibility to a better performance. Presently, these two browsing paradigms are practically incomparable, except by large scale user studies. This makes it infeasible for research groups to evaluate incremental improvement of browsing schemes. Moreover, automated benchmarks in the current literature simulate a user by a model derived directly from the distance measures used within the tested systems. Such a circular reference cannot provide a serious alternative to real user tests. In this paper, we present an automatic benchmark which uses user- annotated collections for simulating the semantic gap, thus providing a means for automatic evaluation and comparison of the different browsing paradigms. We use a very precise annotation of few words together with a thesaurus to provide sufficiently smooth behavior of the annotation-based user model. We discuss the design and evaluation of this annotation as well as the implementation of the benchmark in n MRML-compliant script with pluggable modules which allow testing of new interaction schemes (Multimedia Retrieval Markup Language).

Proceedings Article | 24 August 1999 Paper

Paper

Efficient access methods for content-based image retrieval with inverted files

Henning Mueller, David Squire, Wolfgang Mueller, Thierry Pun

Proceedings Volume 3846, (1999) https://doi.org/10.1117/12.360450

KEYWORDS: Content based image retrieval, Internet, Visualization, Databases, Computing systems, Feature extraction, Legal, Time metrology, Image retrieval, Negative feedback

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Proceedings Article | 24 August 1999 Paper

Paper

Hunting moving targets: extension to Bayesian methods in multimedia databases

Wolfgang Mueller, David Squire, Henning Mueller, Thierry Pun

Proceedings Volume 3846, (1999) https://doi.org/10.1117/12.360437

KEYWORDS: Modeling, Sun, Databases, Image processing, Distance measurement, Bridges, Multimedia, Performance modeling, Systems modeling, Negative feedback

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