The nature of statistical learning theory
The nature of statistical learning theory
Connected morphological operators for binary images
Computer Vision and Image Understanding
Multiscale Connected Operators
Journal of Mathematical Imaging and Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Fast computation of a contrast-invariant image representation
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Identification of erythrocyte types in greyscale MGG images for computer-assisted diagnosis
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Malaria Parasite Detection: Automated Method Using Microscope Color Image
International Journal of E-Health and Medical Communications
Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach
International Journal of Artificial Intelligence and Soft Computing
Hi-index | 0.00 |
Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-consuming task. This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects, from which erythrocytes are found. Finally, a two step classification process identifies infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and average specificity of 91.2%.