0000000000585731

AUTHOR

Marta Zamfir

showing 4 related works from this author

Automatic detection of hemangiomas using unsupervised segmentation of regions of interest

2016

In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…

0301 basic medicineComputer scienceScale-space segmentation02 engineering and technologyOtsu's methodHemangioma03 medical and health sciencessymbols.namesakeMinimum spanning tree-based segmentationRegion of interestHistogram0202 electrical engineering electronic engineering information engineeringmedicineComputer visionSegmentation-based object categorizationbusiness.industryPattern recognitionImage segmentationmedicine.diseaseStatistical classification030104 developmental biologyRegion growingsymbols020201 artificial intelligence & image processingArtificial intelligencebusiness2016 International Conference on Communications (COMM)
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Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators

2016

Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…

Self-organizing mapComputer science050801 communication & media studies02 engineering and technologycomputer.software_genreFuzzy logicImage (mathematics)Hemangioma0508 media and communications0202 electrical engineering electronic engineering information engineeringmedicineLayer (object-oriented design)Cluster analysisFuzzy C-meansGeneral Environmental SciencePixelbusiness.industry05 social sciencesPattern recognitionmedicine.diseasehemangiomaCascadeGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingArtificial intelligenceData miningbusinesscomputerSelf Organizing MapclusteringProcedia Computer Science
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Automatic Monitoring System for the Evolution of the Hemangiomas

2019

In this paper we describe an automatic monitoring system for the evolution of infantile hemangiomas using a fuzzy logic system based on two parameters: area and redness. To follow the evolution, we have used for each subject pairs of images at different moments of time. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Otsu’s method in combination with different preprocessing methods. Using the results of segmentation, we could compute the evolution of the area and the evolution of the redness of hemangioma. These two parameters were used as input for the fuzzy logic system, obt…

Fuzzy logic systemComputer sciencebusiness.industryMonitoring systemPattern recognitionmedicine.diseaseFuzzy logicOtsu's methodHemangiomasymbols.namesakemedicinesymbolsPreprocessorSegmentationArtificial intelligencebusiness2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE)
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Automatic monitoring system for the detection and evaluation of the evolution of hemangiomas

2016

In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Fuzzy C-means. Using the area and the redness of the hemagiomas extracted with Fuzzy C-means, for the same patient, at different moments of time, the algorithm decides whether the hemangioma is evolvin…

0301 basic medicineMatching (graph theory)Computer sciencebusiness.industryFeature extractionFuzzy setMonitoring systemImage segmentationmedicine.diseaseFuzzy logicHemangioma03 medical and health sciences030104 developmental biologymedicineComputer visionArtificial intelligencebusiness2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
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