A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Prostate Ultrasound Image Analysis: Localization of Cancer Lesions to Assist Biopsy
CBMS '95 Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical Systems
International Journal of Hybrid Intelligent Systems
Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients
Advances in Fuzzy Systems - Regular issue
Recent advances in intelligent paradigms fusion and their applications
International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Investigate the Performance of Fuzzy Artmap Classifier for Face Recognition System
SITIS '08 Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
Diagnosing patients with a combination of principal component analysis and case based reasoning
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
Computational Intelligence in Medical Imaging: Techniques and Applications
Computational Intelligence in Medical Imaging: Techniques and Applications
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Rough-Fuzzy granulation, rough entropy and image segmentation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Type-2 fuzzy image enhancement
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Iterated wavelet transformation and signal discrimination for HRR radar target recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper we present an intelligent scheme, employing a combination of fuzzy logic, pulse coupled neural networks (PCNNs), wavelets and rough sets, for analysing prostrate ultrasound images in order diagnose prostate cancer. Image noise is a principal factor which hampers the visual quality of ultrasound images and can therefore lead to misdiagnosis. To address this issue we first utilise an algorithm based on type-II fuzzy sets to enhance the contrast of the image. This is followed by performing PCNN-based segmentation in order to identify the region of interest and to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalised, followed by application of a rough set analysis to discover the dependency between the attributes, and to generate a set of reducts consisting of a minimal number of attributes. Finally, a rough set classifier is designed for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of our approach, we present tests on different prostate ultrasound images. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other intelligent techniques including decision trees, discriminant analysis, rough neural networks, fuzzy ARTMAP, and neural networks.