Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching
Computers in Biology and Medicine
Segmentation of blood and bone marrow cell images via learning by sampling
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Expert Systems with Applications: An International Journal
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
Applying advanced fuzzy cellular neural network AFCNN to segmentation of serial CT liver images
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems
Artificial Intelligence in Medicine
Chaotic cellular neural networks with negative self-feedback
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Automatic recognition of five types of white blood cells in peripheral blood
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Artificial Intelligence in Medicine
Detection of protein conformation defects from fluorescence microscopy images
Engineering Applications of Artificial Intelligence
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White blood cell detection is one of the most basic and key steps in the automatic recognition system of white blood cells in microscopic blood images. Its accuracy and stability greatly affect the operating speed and recognition accuracy of the whole system. But there are only a few methods available for cell detection or segmentation due to the complexity of the microscopic images. This paper focuses on this issue. Based on the detailed analysis of the existing two methods-threshold segmentation followed by mathematical morphology (TSMM), and the fuzzy logic method-a new detection algorithm (NDA) based on fuzzy cellular neural networks is proposed. NDA combines the advantages of TSMM and the fuzzy logic method, and overcomes their drawbacks. With NDA, we can detect almost all white blood cells, and the contour of each detected cell is nearly complete. Its adaptability is strong and the running speed is expected to be comparatively high due to the easy hardware implementation of FCN. Experimental results show good performance