Advances in biomedical image analysis: Past, present and future challenges

Methods of Information in Medicine (Impact Factor: 2.25). 02/2004; 43(4):308-14. DOI: 10.1267/METH04040308
Source: PubMed


Starting from raw data files coding eight bits of gray values per image pixel and identified with no more than eight characters to refer to the patient, the study, and technical parameters of the imaging modality, biomedical imaging has undergone manifold and rapid developments. Today, rather complex protocols such as Digital Imaging and Communications in Medicine (DICOM) are used to handle medical images. Most restrictions to image formation, visualization, storage and transfer have basically been solved and image interpretation now sets the focus of research. Currently, a method-driven modeling approach dominates the field of biomedical image processing, as algorithms for registration, segmentation, classification and measurements are developed on a methodological level. However, a further metamorphosis of paradigms has already started. The future of medical image processing is seen in task-oriented solutions integrated into diagnosis, intervention planning, therapy and follow-up studies. This alteration of paradigms is also reflected in the literature. As German activities are strongly tied to the international research, this change of paradigm is demonstrated by selected papers from the German annual workshop on medical image processing collected in this special issue.

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    • "Analogously in Germany, the workshop “Bildverarbeitung für die Medizin (BVM)” (Image Processing for Medicine) has recently celebrated its 20th annual performance. The meeting has evolved over the years to a multi-track conference on international standard [3, 4, 5, 6, 7, 8, 9]. "
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    ABSTRACT: Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.
    Current Medical Imaging Reviews 05/2013; 9(2):79-88. DOI:10.2174/1573405611309020002 · 0.73 Impact Factor
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    • "The aim of our work is to develop an image analysis system [12] to automatically detect and quantitatively assess pleural thickenings in axial thoracic CT images, hence without any interaction of the user during the calculation processes. For the therapy after a positive diagnosis of pleural mesothelioma, an accurate and reliable documentation of the evolution over time, such as growth rate, is essential. "
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    ABSTRACT: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. For its early stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickenings were provided. Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantify its performance.
    Methods of Information in Medicine 02/2007; 46(3):324-31. DOI:10.1160/ME9050 · 2.25 Impact Factor
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    • "Aims 1 to 5 consider a patientcentered treatment, based on sensor, signal, and imaging informatics. For recent reviews in this fields see, e. g., [10] [11] [12] [13]. Tasks 6 to 10 aim at information and knowledge logistics , as defined 30 years ago. "

    it - Information Technology 01/2006; 48. DOI:10.1524/itit.2006.48.1.3
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