Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
New Developments and Applications of Self-Organizing Maps
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
Watershed Lines Suppression by Waterfall Marker Improvement and Line-Neighbourhood Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Medical Image Segmentation Method Based on Watershed Transform
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Early Experiences in the Staining Pattern Classification of HEp-2 Slides
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
Image retrieval using BDIP and BVLC moments
IEEE Transactions on Circuits and Systems for Video Technology
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Indirect immunofluorescence (IIF) with HEp-2 cells has been used to detect antinuclear auto-antibodies (ANA) for diagnosing systemic autoimmune diseases. The aim of this study is to develop an automatic scheme to identify the fluorescence pattern of HEp-2 cell in the IIF images. By using the previously proposed two-staged segmentation method, the similarity-based watershed algorithm with marker techniques was performed to segment each fluorescence cell. Then the proposed classification method utilized learning vector quantization (LVQ) with eight textural features to identify the fluorescence pattern. This study evaluated 1036 autoantibody fluorescence patterns from 44 IIF images that were divided into six pattern categories (including diffuse, peripheral, coarse speckled, fine speckled, discrete speckled and nucleolar patterns). The simulations show that the proposed system differentiates autoantibody fluorescence patterns with a good result and is therefore clinically useful to provide a second opinion.