Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Image Component Labeling With Watershed Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characteristics Preserving of Ultrasound Medical Images Based on Kernel Principal Component Analysis
Medical Imaging and Informatics
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In this paper, we show the blood-cell image classification system to be able to analyze and distinguish blood cells in the peripheral blood image. To distinguish their abnormalities, we segment red and white-blood cell in an image acquired from microscope with CCD camera and then, apply the various feature extraction algorithms to classify them. In addition to, we use neural network model to reduce multi-variate feature number based on PCA(Principal Component Analysis) to make classifier more efficient. Finally we show that our system has a good experimental result and can be applied to build an aiding system for pathologist.