On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Encoding Based on Extraction of Key Points Using Wavelet Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A rule-based approach for robust clump splitting
Pattern Recognition
Segmentation of malaria parasites in peripheral blood smear images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A domain operator for binary morphological processing
IEEE Transactions on Image Processing
Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images
Expert Systems with Applications: An International Journal
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The presence of clumps in biological cell images may degrade the performance of automated disease detection methods using them. We present techniques to split the clumps based on dominant point detection from contours. The selection of optimal dominant points and split lines is achieved using some logical rules based on geometry of the clumps. The scheme first discards some of the dominant points by processing three successive dominant points and labels the remaining points as split points. The split points are then joined by finding pairs of optimal split points that achieve a good split. Pairs of split points that are opposite to each other are joined first and then the remaining split points are joined by using appropriate heuristics. The performance of the scheme is evaluated using several blood smear images and the results show that the method is capable of handling complex clumps.