Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

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• 作者：Rodrigo Pereira Ramos^a ; ¹ ; ^{rodrigo.ramos@univasf.edu.br} ; ^{godoga@gmail.com} ; Marcelo Zanchetta do Nascimento^b ; ^{marcelo.zanchetta@gmail.com} ; ^{marcelo.nascimento@ufabc.edu.br} ; Danilo Cesar Pereira^b ; ² ; ^{danilo.pereira@ufabc.edu.br}
• 关键词：Mammography ; CADx ; Texture extraction ; Co-occurrence ; Wavelet and ridgelet
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：15 September, 2012
• 卷：39
• 期：12
• 页码：11036-11047
• 文件大小：1385 K

摘要

Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC = 0.90.