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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
A study of cloud classification with neural networks using spectral and textural features
IEEE Transactions on Neural Networks
Improved watershed transform for tumor segmentation: Application to mammogram image compression
Expert Systems with Applications: An International Journal
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
| Hi-index | 12.05 |
This paper presents a genetic based incremental neural network (GINeN) for the segmentation of tissues in ultrasound images. Performances of the GINeN and the Kohonen network are investigated for tissue segmentation in ultrasound images. Feature extraction is carried out by using continuous wavelet transform. Pixel intensities at the same spatial location on 12 wavelet planes and on the original image are considered as features, leading to 13-dimensional feature vectors. The same training set is used for the training of the Kohonen network and the GINeN. This paper proposes the use of wavelet transform and genetic based incremental neural network together in order to increase the segmentation performance. It is observed that genetic based incremental neural network gives satisfactory segmentation performance for ultrasound images.