Search results for "Pattern Recognition"

2015

Background Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. Methods and Results In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively o…

Hereditariness of Aortic Tissue: In-Vitro Time-Dependent Failure of Human and Porcine Specimens

2018

This study aims to investigate the time dependent failure of aortic tissues for pathological and healthy samples by biomechanical testing. The experimental campaign has involved human pathological tissue and healthy swine tissue as preliminary study towards the development of novel failure criteria.

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project.

2016

International audience; Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of …

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI

2015

Purpose To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis. Methods Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance. Results The highest classification accuracy evaluated over test sets was achieved with a subset of ten features…

Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool

2018

Abstract PURPOSE: To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions. METHODS: 61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment a…

Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017)

2018

This book presents 18 carefully selected papers from the ninth edition of the International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017), which was held in Marrakesh, Morocco from December 11 to 13, 2017. A premier conference in the Soft Computing field, SoCPaR brings together the world’s leading researchers and practitioners interested in advancing the state of the art in Soft Computing and Pattern Recognition, allowing them to exchange notes on a broad range of disciplines. The book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering

A physical-computational modelling for analysis of Centromere patterns in IIF images

2014

Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosing autoimmune diseases can be particularly difficult because these disorders can affect any organ or tissue in the body, and produce highly diverse clinical manifestations, depending on the site of autoimmune attack. Moreover, disease symptoms are often not apparent until the disease has reached a relatively advanced stage. These observations suggested us to develop an automated method to support the diagnosis. Developing an automated procedure for diagnosis of autoimmune disease…

Recognition and alignment of variables from UV–vis chromatograms and application to industrial enzyme digests classification

2017

Abstract In the last years, industrial applications of chemometrics have largely increased due to their capacity to extract important information from complex records as chromatograms or spectra data. The use of chemometric methods also can avoid the use of detectors of elevated cost. In this work, a procedure to recognize the relevant chemical information contained in complex UV–vis chromatograms, after a trypsin digestion, to identify the three enzyme main classes (proteases, amylases and cellulases) commonly employed in the cleaning industry, has been developed. In order to recognize the chromatogram peaks, six indices of peak identity or identifiers were defined. A program written in MA…

Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins

2008

Abstract Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils, present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, pre-processing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis…

The Prediction of Human Intestinal Absorption Based on the Molecular Structure

2014

Human Intestinal Absorption (HIA) has been modeled many times by using classification models. However, regression models are scarce. Here, Artificial Neural Networks (ANNs) are implemented for this purpose. A dataset of structurally diverse chemicals with their respective experimental HIA were used to design robust, true predictive and widespread applicable ANN models. An input variables pool was made up of structural invariants calculated by using either Dragon or our software Desmol 1. The selection of best variables was performed following three steps using the entire dataset of molecules. Firstly, variables poorly correlated with the experimental data were eliminated. Secondly, input va…