Search results for "Cells classification"

showing 2 items of 2 documents

A multi-process system for HEp-2 cells classification based on SVM

2016

An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…

Computer scienceSVM02 engineering and technologyImmunofluorescencecomputer.software_genre030218 nuclear medicine & medical imagingImage (mathematics)03 medical and health sciences0302 clinical medicineArtificial IntelligencePyramid0202 electrical engineering electronic engineering information engineeringmedicinePyramid (image processing)Spatial analysisAccuracy1707Contextual image classificationmedicine.diagnostic_testFeatures reductionIndirect immunofluorescencePipeline (software)Class (biology)Settore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)StainingSupport vector machineHep-2 cells classificationSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionData miningcomputerSoftware
researchProduct

Shape-Based Features for Cat Ganglion Retinal Cells Classification

2002

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward’s hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Convex hullSettore INF/01 - InformaticaComputer sciencebusiness.industryFeature extractionPattern recognitionComputational geometryFractal dimensionbody regionsFractalHistogramSignal ProcessingGenetic algorithmComputer visionMedical imagingComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringCells classificationCluster analysisbusinessReal-Time Imaging
researchProduct