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Unsupervised texture segmentation of images using tuned matched Gabor filters
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for classifying a few important kidney categories. Three kidney categories, namely, normal (NR), medical renal diseases (MRD) and cortical cyst (CC) are considered for the analysis. Of the 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by maximizing the similarity between original pre-processed image and reconstructed Gabor image. The dominant Gabor features “$${\mu_{mn}^D }$$” and “$${AAD_{mn}^D }$$” are then evaluated to characterize the tissues of kidney region and compared with the Gabor features derived by considering all Gabor wavelets individually and as a whole using the resultant classification efficiency. The results obtained show that the proposed dominant Gabor wavelet features provide the classification efficiency of 86.66% for NR, 76.66% for MRD and 83.33% for CC, while individual wavelet features offer less than 70%, 63.33% and 66% for NR, MRD and CC. The overall classification efficiency improves by 18.89% with dominant Gabor features when compared to the classification efficiency obtained by considering all the Gabor wavelets features. The outputs of the proposed technique are validated with medical experts to assess the actual efficiency. The overall discriminating ability of the systems is also evaluated with performance evaluation measures, F-score and ROC. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images.