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Chauvin B.,University Paris - Sud | Kasselouri A.,University Paris - Sud | Chaminade P.,University Paris - Sud | Quiameso R.,University Paris - Sud | And 3 more authors.
Analytica Chimica Acta | Year: 2011

Tetrapyrrole rings possess four nitrogen atoms, two of which act as Bröndsted bases in acidic media. The two protonation steps occur on a close pH range, particularly in the case of meso-tetraphenylporphyrin (TPP) derivatives. If the cause of this phenomenon is well known - a protonation-induced distortion of the porphyrin ring - data on stepwise protonation constants and on electronic absorption spectra of monoprotonated TPPs are sparse. A multivariate approach has been systematically applied to a series of glycoconjugated and hydroxylated TPPs, potential anticancer drugs usable in Photodynamic Therapy. The dual purpose was determination of protonation constants and linking substitution with basicity. Hard-modeling version of MCR-ALS (Multivariate Curve Resolution Alternating Least Squares) has given access to spectra and distribution profile of pure components. Spectra of monoprotonated species (H 3TPP +) in solution resemble those of diprotonated species (H 4TPP 2+), mainly differing by a slight blue-shift of bands. Overlap of H 3TPP + and H 4TPP 2+ spectra reinforces the difficulty to evidence an intermediate form only present in low relative abundance. Depending on macrocycle substitution, pK values ranged from 3.5±0.1 to 5.1±0.1 for the first protonation and from 3.2±0.2 to 4.9±0.1 for the second one. Inner nitrogens' basicity is affected by position, number and nature of peripheral substituents depending on their electrodonating character. pK values have been used to establish a predictive Multiple Linear Regression (MLR) model, relying on atom-type electrotopological indices. This model accurately describes our results and should be applied to new TPP derivatives in a drug-design perspective. © 2011 Elsevier B.V.


Rubin D.L.,Stanford University | Willrett D.,Stanford University | O'Connor M.J.,Stanford University | Hage C.,University of Sao Paulo | And 2 more authors.
Translational Oncology | Year: 2014

There are two key challenges hindering effective use of quantitative assessment of imaging in cancer response assessment: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P <.039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response. © 2014 Neoplasia Press, Inc. All rights reserved.


PubMed | Stanford University, CNRS Laboratory of Informatics Paris Descartes and University of Sao Paulo
Type: Journal Article | Journal: Translational oncology | Year: 2014

THERE ARE TWO KEY CHALLENGES HINDERING EFFECTIVE USE OF QUANTITATIVE ASSESSMENT OF IMAGING IN CANCER RESPONSE ASSESSMENT: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.


Berlin I.,French Institute of Health and Medical Research | Jacob N.,Hopital Pitie Salpetriere | Coudert M.,Unite de Recherche Clinique | Perriot J.,Dispensaire Emile Roux | And 2 more authors.
Addiction | Year: 2011

Aims To assess the efficacy of nicotine replacement therapies (NRT) when the daily dose was adapted according to saliva cotinine concentrations. Design Randomized, multi-centre, single-blind, controlled trial. Setting Twenty-one smoking cessation clinics in France. Participants A total of 310 smokers with medical comorbidities, motivated to quit, smoking ≥10 cigarettes/day, for whom smoking cessation was mandatory. NRT was administered for 3 months. The standard care group received nicotine patches with monthly dose decreases; buccal absorption NRT could be co-administered at the discretion of the investigator. In the dose adaptation group, the aim was a 100±5% nicotine substitution with respect to smoking state based on the determination of saliva cotinine concentrations. NRT daily doses were prescribed according to the previous week's saliva cotinine concentrations in the dose adaptation group; saliva cotinine concentrations were not provided in the standard care group. Measurements Prolonged abstinence rate (weeks 9-12, main outcome measure), point-prevalence and continuous abstinence rate, saliva cotinine concentration, NRT daily dose, craving for cigarettes. Findings The median daily prescribed NRT dose was 30 and 31mg/day in the first study week and 17.25 and 35.5mg/day during weeks 9-12 in the standard care group and dose adaptation group, respectively. Saliva cotinine remained stable in the dose adaptation group and decreased in the standard care group (P<0.01) by weeks 9-12. The cotinine substitution rate was significantly lower in the standard care group than in the dose adaptation group. Despite differences in NRT doses and cotinine substitution rates, prolonged (standard care group: 26.4%, dose adaptation group: 30.3%), continuous (standard care group: 8%, dose adaptation group: 12%) and point-prevalence abstinence rates were similar. Conclusions In smokers with medical comorbidities and highly motivated to quit, adaptation of the nicotine replacement therapy daily dose according to saliva cotinine does not appear to be substantially superior to standard nicotine replacement therapy use. © 2011 The Authors, Addiction © 2011 Society for the Study of Addiction.


Othmani A.,French National Center for Scientific Research | Piboule A.,Office National des Forets | Dalmau O.,Research Center En Matematicas Ac | Lomenie N.,CNRS Laboratory of Informatics Paris Descartes | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging. © 2014 Springer-Verlag Berlin Heidelberg.


Pierrot Deseilligny M.,Institute Geographique National | Pierrot Deseilligny M.,CNRS Laboratory of Informatics Paris Descartes | Clery I.,Institute Geographique National
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2011

IGN has developed a set of photogrammetric tools, APERO and MICMAC, for computing 3D models from set of images. This software, developed initially for its internal needs are now delivered as open source code. This paper focuses on the presentation of APERO the orientation software. Compared to some other free software initiatives, it is probably more complex but also more complete, its targeted user is rather professionals (architects, archaeologist, geomophologist) than people. APERO uses both computer vision approach for estimation of initial solution and photogrammetry for a rigorous compensation of the total error; it has a large library of parametric model of distortion allowing a precise modelization of all the kind of pinhole camera we know, including several model of fish-eye; there is also several tools for geo-referencing the result. The results are illustrated on various application, including the data-set of 3D-Arch workshop.


Kurtz C.,CNRS Laboratory of Informatics Paris Descartes | Depeursinge A.,Stanford University | Beaulieu C.F.,Stanford University | Rubin D.L.,Stanford University
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

Image retrieval approaches can assist radiologists by finding similar images in databases as a means to providing decision support. In general, images are indexed using low-level imaging features, and a distance function is used to find the best matches in the feature space. However, using low-level features to capture the appearance of diseases in images is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. We present a semantic framework that enables retrieving similar images based on high-level semantic image annotations. This framework relies on (1) an automatic approach to predict the annotations as semantic terms from Riesz texture image features and (2) a distance function to compare images considering both texture-based and radiodensity-based similarities among image annotations. Experiments performed on CT images emphasize the relevance of this framework. © 2014 IEEE.


Kurtz C.,Stanford University | Kurtz C.,CNRS Laboratory of Informatics Paris Descartes | Beaulieu C.F.,Stanford University | Napel S.,Stanford University | Rubin D.L.,Stanford University
Journal of Biomedical Informatics | Year: 2014

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. © 2014 Elsevier Inc.


Kurtz C.,Stanford University | Kurtz C.,CNRS Laboratory of Informatics Paris Descartes | Depeursinge A.,Stanford University | Napel S.,Stanford University | And 2 more authors.
Medical Image Analysis | Year: 2014

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. © 2014 Elsevier B.V.


Clement M.,CNRS Laboratory of Informatics Paris Descartes | Garnier M.,CNRS Laboratory of Informatics Paris Descartes | Kurtz C.,CNRS Laboratory of Informatics Paris Descartes | Wendling L.,CNRS Laboratory of Informatics Paris Descartes
VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications; VISIGRAPP, Proceedings | Year: 2015

The recognition of complex objects from color images is a challenging task, which is considered as a keystep in image analysis. Classical methods usually rely on structural or statistical descriptions of the object content, summarizing different image features such as outer contour, inner structure, or texture and color effects. Recently, a descriptor relying on the spatial relations between regions structuring the objects has been proposed for gray-level images. It integrates in a single homogeneous representation both shape information and relative spatial information about image layers. In this paper, we introduce an extension of this descriptor for color images. Our first contribution is to consider a segmentation algorithm coupled to a clustering strategy to extract the potentially disconnected color layers from the images. Our second contribution relies on the proposition of new strategies for the comparison of these descriptors, based on structural layers alignments and shape matching. This extension enables to recognize structured objects extracted from color images. Results obtained on two datasets of color images suggest that our method is efficient to recognize complex objects where the spatial organization is a discriminative feature. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved.

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