[Unexplained physical symptoms: a widespread problem but still low-profile in training programs and guidelines].
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Employment↗
Biomedical subjects
H Handels
Publications and source records attributed to H Handels.
Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction.
OBJECTIVES: Spatial-temporal MR image sequences of the heart contain information about shape and motion changes and pathological structures after myocardial infarction. In this paper a Heart Analysis Tool (HeAT) for the quantitative analysis of 4D MR image sequences of infarct patients is presented. METHODS: HeAT supports interactive segmentation of anatomical and pathological structures. Registration of Cine- and DE-MR image data is applied to enable their combined evaluation during the analysis process. Partitioning of the myocardium in segments enables the analysis with high local resolution. Corresponding segments are generated and used for inter/intrapatient comparison. Quantitative parameters were extracted and visualized. RESULTS: Parameters like endocard movement in the infarcted area of six infarct patients were computed in HeAT. Parameters in the infarct area show the expected dysfunctional characteristics. Based on theses parameters passive endocardial movement and myocardial areas with decreased contraction could be identified. CONCLUSION: In contrast to other software tools HeAT supports the combination of contour information of Cine-MR and DE-MR, local analysis with high resolution and inter/intra patient comparison. HeAT enables an observer-independent evaluation of the complex cardiac image data. Using HeAT in further studies can increase the understanding of left ventricle (LV) remodeling.
Algorithms↗
Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters.
OBJECTIVE: This paper describes methods for the automatic atlas-based segmentation of bone structures of the hip, the automatic detection of anatomical point landmarks and the computation of orthopedic parameters to avoid the interactive, time-consuming pre-processing steps for the virtual planning of hip operations. METHODS: Based on the CT data of the Visible Human Data Sets, two three-dimensional atlases of the human pelvis have been built. The atlases consist of labeled CT data sets, 3D surface models of the separated structures and associated anatomical point landmarks. The atlas information is transferred to the patient data by a non-linear gray value-based registration algorithm. A surface-based registration algorithm was developed to detect the anatomical landmarks on the patient's bone structures. Furthermore, a software tool for the automatic computation of orthopedic parameters is presented. Finally, methods for an evaluation of the atlas-based segmentation and the atlas-based landmark detection are explained. RESULTS: A first evaluation of the presented atlas-based segmentation method shows the correct labeling of 98.5% of the bony voxels. The presented landmark detection algorithm enables the precise and reliable localization of orthopedic landmarks. The accuracy of the landmark detection is below 2.5 mm. CONCLUSION: The atlas-based segmentation of bone structures, the atlas-based landmark detection and the automatic computation of orthopedic measures are suitable to essentially reduce the time-consuming user interaction during the pre-processing of the CT data for the virtual three-dimensional planning of hip operations.
Algorithms↗
Three-dimensional planning and simulation of hip operations and computer-assisted construction of endoprostheses in bone tumor surgery.
OBJECTIVE: This article presents the VIRTOPS (VIRTual Operation Planning in Orthopaedic Surgery) software system for virtual preoperative planning and simulation of hip operations. The system is applied to simulate the endoprosthetic reconstruction of the hip joint with hemipelvic replacement, and supports the individual design of anatomically adaptable, modular prostheses in bone tumor surgery. The virtual planning of the operation and the construction of the individual implant are supported by virtual reality techniques. The central step of the operation planning procedure, the placement of the cutting plane in the hip bone, depends strongly on the tumor's position. Segmentation of the tumor and the bones in MR and CT data, as well as fusion of MR and CT image sequences, is necessary to visualize the tumor's position within the hip bone. MATERIALS AND METHODS: Three-dimensional models of the patient's hip are generated based on CT image data. A ROI-based segmentation algorithm enables the separation of the bone tumor in multispectral MR image sequences. A special registration method using segmentation results has been developed to transfer CT and MR data into one common coordinate system. During the 3D planning process, the surgeon simulates the operation and defines the position and geometry of the custom-made endoprosthesis. Stereoscopic visualization and 3D input devices facilitate navigation and 3D interaction in the virtual environment. Special visualization techniques such as texture mapping, color coding of quantitative parameters, and transparency support the determination of the correct position and geometry of the prosthesis. RESULTS AND CONCLUSIONS: The VIRTOPS system enables the complete virtual planning of hip operations with endoprosthetic reconstruction, as well as the optimal placement and design of endoprostheses. After the registration and segmentation of CT and MR data, 3D visualizations of the tumor within the bone are generated to support the surgeon during the planning procedure. In the virtual planning environment, individually adapted endoprostheses can be constructed without the need to generate expensive solid 3D models. Furthermore, different operation strategies can be compared easily. Three-dimensional images and digital movies generated during the virtual operation planning can be used for case documentation and patient information purposes.
Algorithms↗
Atlas-based segmentation of bone structures to support the virtual planning of hip operations.
Two 3-D digitised atlases of a female and a male pelvis were generated to support the virtual 3-D planning of hip operations. The anatomical atlases were designed to replace the interactive, time-consuming pre-processing steps for the virtual operation planning. Each atlas consists of a labelled reference CT data set and a set of anatomical point landmarks. The paper presents methods for the automatic transfer of these anatomical labels to an individual patient data set. The labelled patient data are used to generate 3-D models of the patient's bone structures. Besides the anatomical labelling, the determination of measures, like angles, distances or sizes of contact areas, is important for the planning of hip operations. Thus, algorithms for the automatic computation of orthopaedic parameters were implemented. A first evaluation of the presented atlas-based segmentation method shows a correct labelling of 98.5% of the bony voxels.
Algorithms↗
Simulation of hip operations and design of custom-made endoprostheses using virtual reality techniques.
In this paper a system for the virtual planning of hip operations with endoprosthetic reconstruction and its application in bone tumor surgery is described. The system enables the simulation of the operation and the construction of a custom-made implant depending on the chosen resection planes and the patient's anatomy. During the planning process integrated virtual reality techniques facilitate the interaction with the three-dimensional (3D) medical objects. Stereo viewing improves the perception of the 3D nature of bone structures and tumors. In comparison to conventional planning procedures, different operation strategies and their influence on the geometry of the custom-made endoprosthesis can be easily compared. Furthermore, the combination of multi-modal image information (CT and MR) enables an accurate 3D visualization of the bone tumor within the bone.
Arthroplasty, Replacement, Hip↗
Virtual planning of hip operations and individual adaption of endoprostheses in orthopaedic surgery.
The introduction of virtual reality techniques in medicine opens up new possibilities for the planning of interventions. The presented software system for virtual operation planning in orthopaedic surgery (VIRTOPS) enables the virtual preoperative 3D planning and simulation of pelvis and hip operations. It is used to plan operations of bone tumours with endoprosthetic reconstruction of the hip based on multimodal image information. The operation and the endosprothetic reconstruction of the pelvis are simulated using virtual reality techniques. Stereoscopic visualisation techniques and 3D input devices support the 3D interaction with the virtual 3D models. The main task of the preoperative planning process is the individual design of an anatomically adaptable modular prosthesis. The placement and the design of the endoprosthesis are supported by different functions and visualisation techniques. The resulting 3D images and movies can be used for the documentation of the operation planning procedure, as well as, for the preoperative information of the patient.
Arthroplasty, Replacement, Hip↗
An anatomical atlas to support the virtual planning of hip operations.
Two three-dimensional digitized atlases of a female and a male pelvis were generated to support the virtual 3D-planning of hip operations. Beside the anatomical labeling of bone structures the determination of orthopedic measures, like angles, distances or sizes of contact areas, is important for the planning of hip operations. Thus, each atlas consists of labeled reference CT data sets, a set of landmarks as well as definitions of orthopedic measures and methods for their automatic computation. Furthermore, methods for the automatic transfer of anatomical labels from the atlas to an individual data set are presented resulting in three-dimensional models of the patient's bone structures. The anatomical atlases are designed to replace the interactive, time-consuming pre-processing steps for the virtual 3D operation planning.
Anatomy, Artistic↗
Feature selection for optimized skin tumor recognition using genetic algorithms.
In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
Algorithms↗
Computer-supported diagnosis of melanoma in profilometry.
Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile's structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Furthermore, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.
Diagnosis, Differential↗
Computer-assisted planning and simulation of hip operations using virtual three-dimensional models.
In this paper a new software system for virtual preoperative planning and simulation of hip operations is presented. The system simulates the endoprosthetic reconstruction of the hip joint with hemipelvic replacement for bone tumor patients and supports the individual design of anatomically adaptable, modular prostheses. Three-dimensional models of the patient's hip are generated based on CT data. The surgeon simulates the operation and defines the position and shape of the custom-made endoprosthesis. Stereoscopic visualization and 3D input devices facilitate the navigation and interaction in the virtual environment. Special visualization techniques like texture mapping, color coding of quantitative parameters or transparency support the determination of the correct position and shape of the prosthesis. Furthermore, the system can be used for patient information or educational tasks.
Arthroplasty, Replacement, Hip↗
Activation of the cerebellum by sensory finger stimulation and by finger opposition movements. A functional magnetic resonance imaging study.
Activation of the ipsilateral anterior lobe of the cerebellum by means of hand movements by humans is a well-known phenomenon, but the cerebellar encoding of sensory information has not been well established. The authors delineated the representation of sensory stimulation of fingers in the anterior lobe of the cerebellum using functional magnetic resonance imaging sensitized to changes in blood oxygenation and compared these areas to the regions activated by means of finger opposition movements. Activation was determined by means of pixel-by-pixel correlation of the signal intensity time course with a reference waveform equivalent to the stimulus protocol. All subjects showed significant activation of the anterior lobe of the cerebellum, mainly located in the ipsilateral Larsell lobules IV-V and less consistent in the vermis in relation to sensory finger stimulation. Among some subjects the authors also found activation in the anterior lobe on the contralateral side. The finger movements activated regions that overlapped with the areas activated by sensory finger stimulation but showing a larger and more intense activation pattern.
Adult↗
Image analysis and pattern recognition for computer supported skin tumor diagnosis.
A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 x 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micron. With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. Genetic algorithms are employed successfully to select good feature subsets for the following classification process. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. Classification is performed with feed forward back-propagation network and the nearest neighbor classifier. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result with an error rate of 2.3% was obtained with the nearest neighbor classifier.
Algorithms↗
KAMEDIN: a telemedicine system for computer supported cooperative work and remote image analysis in radiology.
The software system KAMEDIN (Kooperatives Arbeiten und MEdizinische Diagnostik auf Innovativen Netzen) is a multimedia telemedicine system for exchange, cooperative diagnostics, and remote analysis of digital medical image data. It provides components for visualisation, processing, and synchronised audio-visual discussion of medical images. Techniques of computer supported cooperative work (CSCW) synchronise user interactions during a teleconference. Visibility of both local and remote cursor on the conference workstations facilitates telepointing and reinforces the conference partner's telepresence. Audio communication during teleconferences is supported by an integrated audio component. Furthermore, brain tissue segmentation with artificial neural networks can be performed on an external supercomputer as a remote image analysis procedure. KAMEDIN is designed as a low cost CSCW tool for ISDN based telecommunication. However it can be used on any TCP/IP supporting network. In a field test, KAMEDIN was installed in 15 clinics and medical departments to validate the systems' usability. The telemedicine system KAMEDIN has been developed, tested, and evaluated within a research project sponsored by German Telekom.
Humans↗
Image transfer and computer-supported cooperative diagnosis.
The KAMEDIN system was designed as a low-cost communication tool as part of a computer-supported cooperative work project that included synchronized user interaction, telepointing and audioconferencing. During a five-month field trial, it was used for medical image transfer and cooperative diagnosis in 14 clinics and medical departments in Germany. During the field test, 297 teleconsultations were performed via ISDN and 875 MByte of data were transferred. An image compression ratio of 2-3 was obtained, so that the total quantity of data transferred corresponded to 14,000-21,000 magnetic resonance images or 3500-5250 computerized tomography images. Furthermore, 694 local sessions were conducted for the preparation of teleconsultations and the review of transferred images. Participants learned to handle the KAMEDIN system in a few hours. This was mainly owing to the design of the user-oriented graphical user interface and the restriction of the system to a set of essential image-processing functions.
Computers↗
Automatic 3D segmentation and characterization of brain tissues in multiparametric MR image sequences.
In this paper a new segmentation method for automatic differentiation of normal and pathological brain tissues, based on multidimensional relaxation parameter histograms, is introduced. The developed histogram pyramid algorithm, which is an extension of histogram-based cluster analysis methods, is used for automatic analysis of multiparametric image data from several body slices simultaneously. The achieved segmentation results are improved by a following merging algorithm, which merges split tissue parts. Furthermore, tissue specific relaxation parameter values are computed within each 3D tissue segment and can be stored in a data base. Based on the tissue database, automatic tissue classifications are performed using statistical pattern recognition methods. Classified tissues are marked with tissue-specific colors and visualized in tissue class images. For clinical applications, the algorithms for 3D segmentation, visualization, and classification of tissue structures are integrated in the software system SAMSON (System for AutoMatic Segmentation and ClassificatiON of Tissue in Magnetic Resonance Tomography). With the software system SAMSON, a tissue database for intracranial tissues has been established based on the examination of 15 volunteers and 100 patients with neoplastic and other lesions--all verified histologically.
Algorithms↗
ISDN based teleradiology and image analysis with the software system KAMEDIN.
This contribution describes the software system KAMEDIN (Kooperatives Arbeiten und MEdizinische Diagnostik auf Innovativen Netzen) that is designed as an ISDN based computer supported cooperative work (CSCW) tool for usage in medical diagnostics. Medical image data from various sources (for example CT and MR) can be interchanged and analyzed in bilateral teleconferences via ISDN. During a cooperative session, user interactions for image processing etc., are synchronized and performed on both workstations, Features like telepointing, remote control, and audio connection enhance communication quality. With ISDN as a transmission line, widespread availability and low communication costs are achieved. Further, automatic tissue labeling in intracranial MR data can be invoked. For this purpose, artificial neural network classifiers such as multilayer perceptron and Kohenen feature map are integrated. Classification results can be viewed as 3D-reconstructions.
Brain Diseases↗
A new segmentation algorithm for knowledge acquisition in tissue-characterizing magnetic resonance imaging.
Tissue-characterizing magnetic resonance imaging (MRI) is a new imaging method for differentiation and biochemical characterization of tissue based on multidimensional MR-parameter information. To support knowledge acquisition in tissue-characterizing MRI, a new segmentation algorithm has been developed by using clustering techniques. The visualization of the complex biochemical MR-parameter information is performed by extraction of regions with similar biochemical properties. The clustering algorithm leads to an easy and comfortable handling of the complex tissue-characteristic MR information and supports knowledge acquisition for knowledge-based tissue characterization.
Brain↗