Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier

Abstract

Computerized methods have recently shown a great potential in providing radiologists with a second opinion about the visual diagnosis of the malignancy of mammographic masses. The computer-aided diagnosis (CAD) system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented mass. Mass-segmentation plays a key role in most computerized systems. Our technique is a gradient-based one, showing the main characteristic that no free parameters have been evaluated on the data set used in this analysis, thus it can directly be applied to data sets acquired in different conditions without any ad hoc modification. A data set of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses. Sixteen features based on shape, size and intensity of the segmented masses are extracted and analyzed by a multi-layered perceptron neural network trained with the error back-propagation algorithm. The capability of the system in discriminating malignant from benign masses has been evaluated in terms of the receiver-operating characteristic (ROC) analysis. A feature selection procedure has been carried out on the basis of the feature discriminating power and of the linear correlations interplaying among them. The comparison of the areas under the ROC curves obtained by varying the number of features to be classified has shown that 12 selected features out of the 16 computed ones are powerful enough to achieve the best classifier performances. The radiologist assigned the segmented masses to three different categories: correctly-, acceptably- and non-acceptably-segmented masses. We initially estimated the area under ROC curve only on the first category of segmented masses (the 88.5% of the data set), then extending the classification to the second subclass (reaching the 97.8% of the data set) and finally to the whole data set, obtaining A(z)=0.805+/-0.030, 0.787+/-0.024 and 0.780+/-0.023, respectively.

Presentation of similar images for diagnosis of breast masses on mammograms: analysis of the effect on residents

“…With the hope of helping radiologists in improving the diagnostic accuracy by providing useful information, computerized analysis of breast lesions on mammograms has been studied by many investigators [9][10][11][12][13][14][15][16][17]. The results of observer performance studies indicated the potential usefulness of the presentation of the likelihood of malignancy of lesions in radiologists' classification of benign and malignant masses [18,19] and clustered microcalcifications [20].…”

Automatic Diagnosis of Breast Tissue

“…We will also show how the IA can be efficient to differentiate malignant mass from benign ones. On the other hand, the RDM descriptor (Alvarenga et al, 2006) (Delogu et al, 2008) was taken a great importance in medical imaging litterature. It is based on the computation of the distances between contour points and gravity center of the region.…”

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

“…The features can be calculated from the mass region contain such as the density, texture, morphologic, shape, and size [5,6,10,11]. Besides, several methods using multiresolution analysis have been proposed for feature extraction in mammograms [10,12].…”

“…The classifier is a tool that provides as output the risk degree related to the tissue. Classifiers like support vector machine (SVM) [5,6,12,13], decision tree [5], neural network [5,11], and linear discriminant analysis (LDA) [5,14] have been widely used and performed well. Wu et al [6] segmented the breast tumor by level set method, and the auto-covariance texture features and morphologic features were extracted, then they used the genetic algorithm to detect the significant features and identified the tumors by SVM, which get the accuracy of 95.24 %.…”

“…Wu et al [6] segmented the breast tumor by level set method, and the auto-covariance texture features and morphologic features were extracted, then they used the genetic algorithm to detect the significant features and identified the tumors by SVM, which get the accuracy of 95.24 %. Delogu [11] employed a gradient-based algorithm to segment the mass, and 12 features were selected from the extracted 16 features based on shape, size, and intensity and, as the input of a neural classifier, finally obtained the highest classification accuracy of 97.8 % on the acceptably segmented masses assigned by radiologists.…”