Journal images represent an important part of the knowledge stored in the medical literature. Figure classification has received much attention as the information of the image types can be used in a variety of contexts to focus image search and filter out unwanted information or "noise", for example non-clinical images. A major problem in figure classification is the fact that many figures in the biomedical literature are compound figures and do often contain more than a single figure type. Some journals do separate compound figures into several parts but many do not, thus requiring currently manual separation.In this work, a technique of compound figure separation is proposed and implemented based on systematic detection and analysis of uniform space gaps. The method discussed in this article is evaluated on a dataset of journal figures of the open access literature that was created for the ImageCLEF 2012 benchmark and contains about 3000 compound figures.Automatic tools can easily reach a relatively high accuracy in separating compound figures. To further increase accuracy efforts are needed to improve the detection process as well as to avoid over-separation with powerful analysis strategies. The tools of this article have also been tested on a database of approximately 150'000 compound figures from the biomedical literature, making these images available as separate figures for further image analysis and allowing to filter important information from them.
The growth of the amount of medical image data produced on a daily basis in modern hospitals forces the adaptation of traditional medical image analysis and indexing approaches towards scalable solutions. The number of images and their dimensionality increased dramatically during the past 20 years. We propose solutions for large-scale medical image analysis based on parallel computing and algorithm optimization. The MapReduce framework is used to speed up and make possible three large-scale medical image processing use-cases: (i) parameter optimization for lung texture segmentation using support vector machines, (ii) content-based medical image indexing, and (iii) three-dimensional directional wavelet analysis for solid texture classification. A cluster of heterogeneous computing nodes was set up in our institution using Hadoop allowing for a maximum of 42 concurrent map tasks. The majority of the machines used are desktop computers that are also used for regular office work. The cluster showed to be minimally invasive and stable. The runtimes of each of the three use-case have been significantly reduced when compared to a sequential execution. Hadoop provides an easy-to-employ framework for data analysis tasks that scales well for many tasks but requires optimization for specific tasks.
SummaryObjectives: The main objective of this study is to learn more on the image use and search requirements of radiologists. These requirements will then be taken into account to develop a new search system for images and associated meta data search in the Khresmoi project. Methods: Observations of the radiology workflow, case discussions and a literature review were performed to construct a survey form that was given online and in paper form to radiologists. Eye tracking was performed on a radiology viewing station to analyze typical tasks and to complement the survey. Results: In total 34 radiologists answered the survey online or on paper. Image search was mentioned as a frequent and common task, particularly for finding cases of interest for differential diagnosis. Sources of information besides the Internet are books and discussions with colleagues. Search for images is unsuccessful in around 25% of the cases, stopping the search after around 10 minutes. The most common reason for failure is that target images are considered rare. Important additions for search requested in the survey are filtering by pathology and modality, as well as search for visually similar images and cases. Few radiologists are familiar with visual retrieval but they desire the option to upload images for searching similar ones. Conclusions: Image search is common in radiology but few radiologists are fully aware of visual information retrieval. Taking into account the many unsuccessful searches and time spent for this, a good image search could improve the situation and help in clinical practice. Methods Inf Med 6/2012 539
The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems.
Large amounts of medical images are being produced to help physicians in diagnosis and treatment planning. These images are then archived in PACS (Picture Archival and Communication Systems) and usually they are only reused in the context of the same patient during further visits. Medical image retrieval systems allow medical professionals to search for images in institutional archives, the Internet or in the scientific literature. The goal of the search can be in diagnosis but often as well for teaching and research. A large body of research has investigated efficient and effective algorithms to retrieve a set of images to fulfil a specific information need. However, much less research has been done on studying simple and engaging interaction for users of medical image retrieval systems. In this paper we propose an intuitive and engaging web-based interface targeted to be used by a large range of users with gesture control. This interface allows users to retrieve medical images by accessing a system called Parallel Distributed Image Search Engine (ParaDISE), a text-and content-based image retrieval system. Accepting search with keywords and example images, this interface uses simple gestures to get random example images and mark examples as positive and negative relevance feedback with results being updated after each interaction.
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