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
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Segmentation of kidney from ultrasound B-mode images with texture-based classification
Computer Methods and Programs in Biomedicine
Advanced fuzzy cellular neural network: Application to CT liver images
Artificial Intelligence in Medicine
Segmentation of medical images by using wavelet transform and incremental self-organizing map
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Image clustering using improved spatial fuzzy C-means
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Neuro fuzzy and punctual kriging based filter for image restoration
Applied Soft Computing
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Automatic RNA virus classification using the Entropy-ANFIS method
Digital Signal Processing
An efficient neural network based method for medical image segmentation
Computers in Biology and Medicine
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In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.