Classification of Ultrasound Kidney Images using PCA and Neural Networks

In this paper, a computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases. Images of five classes: Normal, Cyst, Stone, Tumor and Failure were considered. A set of statistical features and another set of multi-scale wavelet-based features were extracted from the r...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2015-01, Vol.6 (4)
Main Authors: Wagih, Mariam, Abou-Chadi, F.E.Z., El-Din, Hossam, Mekky, Nagham
Format: Article
Language:English
Subjects:
Feature extraction
Image classification
Kidney diseases
Neural networks
Principal components analysis
Ultrasonic imaging
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Summary:In this paper, a computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases. Images of five classes: Normal, Cyst, Stone, Tumor and Failure were considered. A set of statistical features and another set of multi-scale wavelet-based features were extracted from the region of interest (ROI) of each image and the principal component analysis was performed to reduce the number of features. The selected features were utilized in the design and training of a neural network classifier. A correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2015.060407