Texture Classification of the Ultrasonic Images of Rotator Cuff Diseases based on Radial Basis Function Network
ABSTRACT This article studies the usages of texture analysis methods to classify ultrasonic rotator cuff images into the different disease groups that are normal, tendon inflammation, calcific tendonitis and tendon tear. The adopted texture analysis methods include the texture feature coding method, gray-level co-occurrence matrix, fractal dimension and texture spectrum. The texture features of the four methods are used to analyze the tissue characteristic of supraspinatus tendon. The mutual information feature selection and F-scoring feature ranking method are independently used to select powerful features from the four texture analysis methods. Furthermore, the trained radial basis function network is used to classify the test images into the ones of four disease group. Experimental results tested on 85 images reveal that the classification accuracy of proposed system can achieves 84%.
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ABSTRACT: Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.IEEE Transactions on Pattern Analysis and Machine Intelligence 01/2003; 24(12):1667- 1671. DOI:10.1109/TPAMI.2002.1114861 · 5.69 Impact Factor
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ABSTRACT: This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is suitable for assessing the “information content” of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a “greedy” selection of the features and that takes both the mutual information with respect to the output class and with respect to the already-selected features into account. Finally the results of a series of experiments are discussedIEEE Transactions on Neural Networks 08/1994; 5(4-5):537 - 550. DOI:10.1109/72.298224 · 2.95 Impact Factor
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ABSTRACT: Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.Pattern Recognition Letters 12/2006; DOI:10.1016/j.patrec.2006.05.008 · 1.55 Impact Factor