Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cell Segmentation with Median Filter and Mathematical Morphology Operation
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Model-based Segmentation of Leukocytes Clusters
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
System-level training of neural networks for counting white bloodcells
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic cervical cell segmentation and classification in Pap smears
Computer Methods and Programs in Biomedicine
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An automatic segmentation technique for microscopic bone marrow white blood cell images is proposed in this paper. The segmentation technique segments each cell image into three regions, i.e., nucleus, cytoplasm, and background. We evaluate the segmentation performance of the proposed technique by comparing its results with the cell images manually segmented by an expert. The probability of error in image segmentation is utilized as an evaluation measure in the comparison. From the experiments, we achieve good segmentation performances in the entire cell and nucleus segmentation. The six-class cell classification problem is also investigated by using the automatic segmented images. We extract four features from the segmented images including the cell area, the peak location of pattern spectrum, the first and second granulometric moments of nucleus. Even though the boundaries between cell classes are not well-defined and there are classification variations among experts, we achieve a promising classification performance using neural networks with five-fold cross validation.