Introduction to non-linear optimization
Introduction to non-linear optimization
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Feedforward Neural Network Methodology
Feedforward Neural Network Methodology
Digital Image Processing
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Neural Networks
Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
A differential adaptive learning rate method for back-propagation neural networks
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial neural network-statistical approach for PET volume analysis and classification
Advances in Fuzzy Systems - Special issue on Hybrid Biomedical Intelligent Systems
Hi-index | 0.00 |
Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.