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					<title>Communications Engineering Branch of the U.S. National Library of Medicine</title>
					<link>http://archive.nlm.nih.gov</link>
					<description>Research focus: biomedical image processing, document image analysis and understanding, automated extraction of bibliographic data from journals, content-based image indexing.</description>
					<language>en-us</language>
					<pubDate>Wed, 22 May 2013 16:31:24 GMT</pubDate>
                    <lastBuildDate>Wed, 22 May 2013 16:31:24 GMT</lastBuildDate>
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						<title>Communications Engineering Branch (CEB) Logo</title> 
						<url>http://archive.nlm.nih.gov/cebimages/feed.jpg</url>
						<link>http://archive.nlm.nih.gov</link>
						<width>64</width> 
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						 <title><![CDATA[Fast GPU-based Segmentation of H&E Stained Squamous Epithelium From Multi-gigapixel Tiled Virtual Slides.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008182</link>
						 <description><![CDATA[The processing of multi-gigapixel virtual histology slides presents a computationally intensive and time consuming task. Common tiled TIFF slide formats, such as those used by Aperio [1], contain inherent header information that can be used to rapidly locate tissue regions for cervical intraepithelial neoplasia (CIN) diagnosis. Tiles used in these formats are individually compressed subsections of the virtual slide, whose compression ratio varies based on their individual content. This paper discusses a method that exploits this information to rapidly identify regions of interest in an iterative process to locate epithelial tissue. These regions are decompressed using a multi-core CPU, from which a Compute Unified Device Architecture (CUDA) enabled GPU rapidly generates features and Support Vector Machine (SVM) decisions. SVM classifier results are used in a post-processing scheme to remove apparently spurious misclassifications. The mean overall execution time when using a high-end desktop PC, together with a GTX 560 GPU, is roughly 3 seconds per gigapixel, while maintaining the area under an ROC curve above 0.9 when classifying squamous epithelium versus other tissues.]]></description>
						 <pubDate>Wed, 10 Apr 2013 13:35:01 GMT</pubDate>
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						 <title><![CDATA[Customized Hybrid Level Sets For Automatic Lung Segmentation In Chest X-ray Images.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008184</link>
						 <description><![CDATA[A chest x-ray screening system for pulmonary pathologies such as tuberculosis (TB) is of paramount importance due to the increasing mortality rate of patients with undiagnosed TB, especially in densely-populated developing countries. As a first step toward developing such screening systems, this paper presents a novel computer vision module that automatically segments the lungs from posteroanterior digital chest x-ray images. The segmentation task is non-trivial, due to poor image contrast and occlusion of the lung region by ribs, clavicle, heart, and by non-TB abnormalities associated with pulmonary diseases. In the proposed procedure, we first compute a lung shape model by employing a level set based technique for registration up to a homography. Next, we use this computed mean lung shape to initialize the level set that is based on a best fit measure obtained in a heuristically estimated search space for the projective transform parameters. Once the level set is initialized, a suite of customized lower level image features and higher level shape features up to a homography evolve the level set function at a lower resolution in order to achieve a coarse segmentation of the lungs. Finally, a fine segmentation step is performed by adding additional shape variation constraints and evolving the level set in a higher resolution. We processed the standard Japanese Society of Radiological Technology (JSRT) dataset, comprised of 247 images, using this scheme. The promising results (92% accuracy) demonstrate the viability and efficacy of the proposed approach.]]></description>
						 <pubDate>Wed, 10 Apr 2013 17:13:06 GMT</pubDate>
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						 <title><![CDATA[Integrating Shape Into an Interactive Segmentation Framework.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008183</link>
						 <description><![CDATA[This paper presents a novel interactive annotation toolbox which extends a well-known user-steered segmentation framework, namely Intelligent Scissors (IS). IS, posed as a shortest path problem, is essentially driven by lower level image based features. All the higher level knowledge about the problem domain is obtained from the user through mouse clicks. The proposed work integrates one higher level feature, namely shape up to a rigid transform, into the IS framework, thus reducing the burden on the user and the subjectivity involved in the annotation procedure, especially during instances of occlusions, broken edges, noise and spurious boundaries. The above mentioned scenarios are commonplace in medical image annotation applications and, hence, such a tool will be of immense help to the medical community. As a first step, an offline training procedure is performed in which a mean shape and the corresponding shape variance is computed by registering training shapes up to a rigid transform in a level-set framework. The user starts the interactive segmentation procedure by providing a training segment, which is a part of the target boundary. A partial shape matching scheme based on a scale-invariant curvature signature is employed in order to extract shape correspondences and subsequently predict the shape of the unsegmented target boundary. A 'zone of confidence' is generated for the predicted boundary to accommodate shape variations. The method is evaluated on segmentation of digital chest x-ray images for lung annotation which is a crucial step in developing algorithms for screening Tuberculosis.]]></description>
						 <pubDate>Wed, 10 Apr 2013 13:38:45 GMT</pubDate>
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						 <title><![CDATA[Text- and Content-based Biomedical Image Modality Classification.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008185</link>
						 <description><![CDATA[No abstract available.]]></description>
						 <pubDate>Wed, 10 Apr 2013 17:14:52 GMT</pubDate>
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						 <title><![CDATA[Research Designs In Mesh and Emtree: a Comparative Study of Coverage.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008167</link>
						 <description><![CDATA[To aid retrieval of studies common to comparative effectiveness research (CER), we developed a terminology based
on the local terminologies of several organizations. We compared coverage of the new terminology in MeSH and
Emtree, and developed a crosswalk between the two controlled vocabularies. Patterns of coverage were similar and
partial matches predominated. Negated or detailed terms were rarely matched exactly. For unmapped or partially
mapped designs, records were retrievable if a substring in a design query matched the language of scientists.]]></description>
						 <pubDate>Sat, 01 Dec 2012 17:18:34 GMT</pubDate>
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						 <title><![CDATA[The Role of Location For Family
Reunification During Disasters.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008158</link>
						 <description><![CDATA[After large-scale disasters, displaced or injured people can lose contact with their family and friends. In an effort to mitigate the effects of these events, the US National Library of Medicine has developed People Locator, a Web-based system that allows family members to search for missing persons. The purpose of this paper is to describe the role of location in family reunification systems, in particular in People Locator, and the data input technologies that support it.]]></description>
						 <pubDate>Sat, 17 Nov 2012 13:18:29 GMT</pubDate>
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						 <title><![CDATA[Towards the Creation of a Visual Ontology of Biomedical Imaging Entities.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008162</link>
						 <description><![CDATA[Image content is frequently the target of biomedical information extraction systems. However, the meaning of this
content cannot be easily understood without some associated text. In order to improve the integration of textual and
visual information, we are developing a visual ontology for biomedical image retrieval. Our visual ontology maps
the appearance of image regions to concepts in an existing textual ontology, thereby inheriting relationships among
the visual entities. Such a resource creates a bridge between the visual characteristics of important image regions
and their semantic interpretation. We automatically populate our visual ontology by pairing image regions with their
associated descriptions. To demonstrate the usefulness of this resource, we have developed a classification method that
automatically labels image regions with appropriate concepts based solely on their appearance. Our results for thoracic
imaging terms show that our methods are promising first steps towards the creation of a biomedical visual ontology.]]></description>
						 <pubDate>Fri, 30 Nov 2012 18:50:22 GMT</pubDate>
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						 <title><![CDATA[Graph Cut Based Automatic Lung Boundary Detection
In Chest Radiographs.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008160</link>
						 <description><![CDATA[The National Library of Medicine (NLM) is developing
a digital chest x-ray (CXR) screening system for
deployment in resource constrained communities. An important
first step in the analysis of digital CXRs is the automatic
detection of the lung regions. In this paper, we present a graph
cut based robust lung segmentation method that detects the
lungs with high accuracy. The method consists of two stages: (i)
average lung shape model calculation, and (ii) lung boundary
detection based on graph cut. Preliminary results on public
chest x-rays demonstrate the robustness of the method.]]></description>
						 <pubDate>Wed, 21 Nov 2012 11:41:47 GMT</pubDate>
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						 <title><![CDATA[Window Classification of Brain CT Images In Biomedical Articles.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008157</link>
						 <description><![CDATA[Effective capability to search biomedical articles based on visual properties of article images may significantly
augment information retrieval in the future. In this paper, we present a new method to classify the window setting
types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to
enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides
radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone
dysplasia which are usually examined using the bone window setting and illustrated in biomedical articles using bone window images. Due to the inherent large variations of images among articles, it is important that the
proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or nonbone
window in a 210 image data set.]]></description>
						 <pubDate>Wed, 14 Nov 2012 14:37:58 GMT</pubDate>
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						 <title><![CDATA[Using NLP and Manually Validated Data From Clinical Notes To Produce Unbiased Estimates of Smoking Cancer Risk.]]></title>
						 <link>http://archive.nlm.nih.gov/pubs/abstract/docabstract.php?docID=2008147</link>
						 <description><![CDATA[Using NLP and manually validated data from clinical notes to produce unbiased
estimates of smoking cancer risk.]]></description>
						 <pubDate>Thu, 18 Oct 2012 19:13:43 GMT</pubDate>
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