International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Short communication: The use of Boolean model for texture analysis of grey images
Computer Vision and Image Understanding
Image Processing: The Fundamentals
Image Processing: The Fundamentals
Multi-interval Discretization Methods for Decision Tree Learning
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Prototype-based classification
Applied Intelligence
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Mining Lung Shape from X-Ray Images
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Analysis and Classification of Crithidia Luciliae Fluorescent Images
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Data & Knowledge Engineering
Fusion of systems for automated cell phenotype image classification
Expert Systems with Applications: An International Journal
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Image acquisition and analysis of hazardous biological material in air
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Finding cells, finding molecules, finding patterns
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
HEp-2 cell classification in indirect immunofluorescence image
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A decision support system for Crithidia Luciliae image classification
Artificial Intelligence in Medicine
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Mitotic HEp-2 cells recognition under class skew
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
HEp-2 cell pattern segmentation for the support of autoimmune disease diagnosis
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
A comparative study of catalogue-based classification
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
HEp-2 cell images classification based on textural and statistic features using self-organizing map
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
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HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns has recently been done manually by a human inspecting the slides with a microscope. In this paper, we present results on the analysis and classification of cells using image analysis and data mining techniques. Starting from a knowledge acquisition process with a human operator, we developed an image analysis and feature extraction algorithm. The collection of the dataset was done based on an expert's image reading and based on the automatic extracted features. A dataset containing 132 features for each entry was set up and given to a data mining algorithm to find out the relevant features among this large feature set and to construct the classification knowledge. The classifier was evaluated by cross validation. The results gave the expert new insights into the necessary features and the classification knowledge and show the feasibility of an automated inspection system.