Search results for "algorithm"
showing 10 items of 4887 documents
Comparing normal means: new methods for an old problem
2007
Comparing the means of two normal populations is an old problem in mathematical statistics, but there is still no consensus about its most appropriate solution. In this paper we treat the problem of comparing two normal means as a Bayesian decision problem with only two alternatives: either to accept the hypothesis that the two means are equal, or to conclude that the observed data are, under the assumed model, incompatible with that hypothesis. The combined use of an information-theory based loss function, the intrinsic discrepancy (Bernardo and Rueda 2002}, and an objective prior function, the reference prior \citep{Bernardo 1979; Berger and Bernardo 1992), produces a new solution to this…
Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis
2006
Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…
FABC: Retinal Vessel Segmentation Using AdaBoost
2010
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…
A completely automated CAD system for mass detection in a large mammographic database.
2006
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…
Fuzzy technique for microcalcifications clustering in digital mammograms
2012
Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …
Ligand-based discovery of novel trypanosomicidal drug-like compounds: In silico identification and experimental support
2010
Abstract Two-dimensional bond-based linear indices and linear discriminant analysis are used in this report to perform a quantitative structure–activity relationship study to identify new trypanosomicidal compounds. A database with 143 anti-trypanosomal and 297 compounds having other clinical uses, are utilized to develop the theoretical models. The best discriminant models computed using bond-based linear indices provides accuracies greater than 90 for both training and test sets. Our models identify as anti-trypanosomals five out of nine compounds of a set of already-synthesized substances. The in vitro anti-trypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi…
Exudate-based diabetic macular edema detection in fundus images using publicly available datasets
2010
International audience; Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publi…
Deployment of the ATLAS high level trigger
2005
The ATLAS combined test beam in the second half of 2004 saw the first deployment of the ATLAS high-level triggers (HLT). The next steps are deployment on the pre-series farms in the experimental area during 2005, commissioning and cosmics tests in 2006 and collisions in 2007. This paper reviews the experience gained in the test beam, describes the current status and discusses the further enhancements to be made. We address issues related to the dataflow, selection algorithms, testing, software distribution, installation and improvements
Genomic determinants of speciation and spread of the Mycobacterium tuberculosis complex
2019
14 páginas, 6 figuras
Iterative Symmetry Detection: Shrinking vs. Decimating Patterns
2005
This paper introduces a new mechanism that consists of applying a symmetry operator on an iteratively transformed version of the input image. The nature of the transformation characterizes the operator. Here, we consider the Object Symmetry Transform combined with the morphological operator erosion and the pyramid decimation respectively. The derived operators have been applied on both binary and gray levels images, comparing their ability to grasp the internal structure of a digital object. We present some experiments to evaluate their performances and check them for result quality versus computing complexity.