Segmentation of ultrasound images by using a hybrid neural network
Pattern Recognition Letters
Image segmentation based on situational DCT descriptors
Pattern Recognition Letters
Bayesian Fusion of Color and Texture Segmentations
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bayesian segmentation of hepatic biopsy color images in the JPEG compressed domain
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
RK-Means Clustering: K-Means with Reliability
IEICE - Transactions on Information and Systems
Image retrieval for alzheimer's disease detection
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Image segmentation is a prerequisite process for image content understanding and visual object recognition in medical images for the development of a computer aided diagnosis(CAD) system. An unsupervised segmentation method is proposed which uses discrete cosine transform (DCT) coefficients for extraction of feature vectors and the Fisher Discriminant K-means (FDK) technique for clustering image pixels. In this study, the parenchymal region in HRCT lung images is separated first and then feature vectors using the deviation in local variance in DCT coefficients are determined for each pixels of parenchyma regions. The extracted feature vectors are used for selection of the best feature sets by reducing the dimensionality of the feature vector. The reduced feature vector is used for unsupervised classification using the K-means clustering algorithm which is guided by Fisher linear discriminant parameters for determining number of distinguishable regions in the image.