Fundamentals of digital image processing
Fundamentals of digital image processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Detecting texture periodicity from the co-occurrence matrix
Pattern Recognition Letters
Image characterizations based on joint gray level-run length distributions
Pattern Recognition Letters
Features and classification methods to locate deciduous trees in images
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Segmentation of ultrasound images by using a hybrid neural network
Pattern Recognition Letters
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Segmentation of Ultrasound Images by Using Quantizer Neural Network
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
IEEE Transactions on Computers
A study of cloud classification with neural networks using spectral and textural features
IEEE Transactions on Neural Networks
Heart Cavity Segmentation in Ultrasound Images Based on Supervised Neural Networks
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
A neural approach to image thresholding
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.