Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

Al-Qaheri

Schaefer

et al. 2009

Self Cite

In this paper, we present an intelligent approach to analysing prostrate ultrasound images in order to diagnose prostate cancer. Algorithms based on fuzzy image processing are applied first to enhance the contrast of the original image, to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterising the underlying texture of the regions of interest based wavelet domain features. Finally, a rough neural network, where a neural network and rough set theory are integrated into a hybrid system, is designed for discrimination of different regions of interest to test whether they represent malignant or benign cases. The neural network is built from rough neurons, each of which can be viewed as a pair of subneurons, corresponding to the lower and upper bound concepts of rough set theory. Experimental results show that the overall classification accuracy of our approach is high.Keywords-Prostate cancer, ultrasound imaging, fuzzy logic, neural networks, wavelets, rough set theory, intelligent hybrid approach. 978-1-4244-5612-3/09/$26.00 c 2009 IEEE

Section: Data Analysis Phasementioning

confidence: 99%

Intelligent analysis of prostate ultrasound images

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

Al-Qaheri

Schaefer

et al. 2009

Self Cite

“…The Eqs (1)(2)(3)(4)(5) are run iteratively until the user decides to stop. There is currently no stop mechanism into PCNN.…”

Section: Pulse-coupled Neural Network (Pcnn)mentioning

confidence: 99%

Intelligence techniques for prostate ultrasound image analysis

2009

HIS

Self Cite

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.

“…Since the former perform poorly in many situations because they ignore the class of the training examples, a typical supervised method, χ 2 -test also named ChiMerge algorithm is used to discretize pixel grey values. Another example is to use the rough set with boolean reasoning (RSBR) algorithm for the discretization of continuous-valued attributes proposed by Nguyen in 1997 26 (see, also, 3,18,47 Define a set B of boolean variables in (9).…”

Section: Rough Discretization Processmentioning

confidence: 99%

“…6 . In this paper we applied the reduct algorithm based on the degree of dependencies and the discrimination factors 3 . The main steps of the reduct generation algorithm are provided below:…”

Section: Computation Of the Reductsmentioning

confidence: 99%

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Int. J. Comp. Intel. Appl.

Abraham

2008

The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant or benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.