Classification Of Masses In Mammograms Research Articles

A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN

A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN

Texton analysis for mass classification in mammograms

In this paper, a novel method is proposed to classify masses as benign or malignant in mammograms without segmenting the contour of each mass. This method combines the scheme of texton analysis with multiple subsampling strategies. Before performing texton-based classification, intensity and rotation normalization are applied. Then subsampling strategies with either uniform or non-uniform intervals are generated and a k-nearest-neighbor (KNN) classifier is trained for each subsampling strategy. Every subsampling strategy captures one discriminating structure. To take advantage of all possible discriminating structures, the final classification result is obtained by performing majority voting over all subsampling strategies. The proposed method is tested on 114 mass regions from Digital Database for Screening Mammography (DDSM) database. The classification accuracy rate reaches 85.96%, which is higher than some existing texture-based methods.

Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method

Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method

Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method

Masses are the primary indications of breast cancer in mammograms, and it is important to classify them as benign or malignant. Benign and malignant masses differ in geometry and texture characteristics. However, not every geometry and texture feature that is extracted contributes to the improvement of classification accuracy; thus, to select the best features from a set is important. In this paper, we examine the feature selection methods for mass classification. We integrate a support vector machine (SVM)-based recursive feature elimination (SVM-RFE) procedure with a normalized mutual information feature selection (NMIFS) to avoid their singular disadvantages (the redundancy in the selected features of the SVM-RFE and the unoptimized classifier for the NMIFS) while retaining their advantages, and we propose a new feature selection method, which is called the SVM-RFE with an NMIFS filter (SRN). In addition to feature selection, we also study the initialization of mass segmentation. Different initialization methods are investigated, and we propose a fuzzy c-means (FCM) clustering, with spatial constraints as the initialization step. In the experiments, 826 regions of interest (ROIs) from the Digital Database for Screening Mammography were used. All 826 were used in the classification experiments, and 413 ROIs were used in the feature selection experiments. Different feature selection methods, including F-score, Relief, SVM-RFE, SVM-RFE with a minimum redundancy-maximum relevance (mRMR) filter [SVM-RFE (mRMR)], and SRN, were used to select features and to compare mass classification results using the selected features. In the classification experiments, the linear discriminant analysis and the SVM classifiers were investigated. The accuracy that is obtained with the SVM classifier using the selected features obtained by the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 88%, 88%, 90%, 91%, and 93%, respectively, with a tenfold cross-validation procedure, and 91%, 89%, 92%, 92%, and 94%, respectively, with a leave-one-out (LOO) scheme. We also compared the performance of the different feature selection methods using the receiver operating characteristic analysis and the areas under the curve (AUCs). The AUCs for the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 0.9014, 0.8916, 0.9121, 0.9236, and 0.9439, respectively, with a tenfold cross-validation procedure, and are 0.9312, 0.9178, 0.9324, 0.9413, and 0.9615, respectively, with a LOO scheme. Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance. In addition to the accuracy and the AUC, we also measured the significance between the two best feature selection methods, i.e., the SVM-RFE (mRMR) and the proposed SRN method. Experimental results show that the proposed SRN method is significantly more accurate than the SVM-RFE (mRMR) (p = 0.011).

Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer

This paper focuses on an approach for characterising the mammogram masses using various geometric shape and margin features. According to BIRADS system, benign and malignant masses can be differentiated using its shape, size and density features, which is how radiologist visualise the mammograms. According to BIRADS, benign masses are round, oval, lobular in shape and malignant masses are lobular or irregular in shape. Various 17 geometrical shape and margin features are introduced to characterise the morphology of masses, as there is no single measure to differentiate various shapes. Experiments have been conducted on 1553 DDSM database mammograms and classified using CART classifier. Experimental results indicate that CART can classify masses effectively and generates simple rules, which can be easily implemented in any system using if..then..else statements. Experimental results are found to be encouraging. The results demonstrate the effectiveness of CART classifier for classifying masses as benign, malignant and normal.

A hybrid classifier for mammogram mass classification using various new geometric shape and margin features

In this paper, a hybrid classifier system is proposed, which combines statistical classifier analysis of variance (ANOVA) discriminant analysis (ADA) and neural network (NN)/radial basis function network (RBFN). According to breast imaging reporting and data system, the benign and malignant masses can be characterised by its shape, size and margins. Benign masses tend to have round, oval, lobular shapes and malignant masses are lobular or irregular in shape. Various 17 geometrical shape and margin features were introduced to characterise the morphology of masses. The mammogram mass is classified as either benign or malignant. The proposed features do not rely on grey values of the masses, which make it effective compared to statistical measures. The ADA classifier produces discriminant function score, which is used as extra feature combined with existing 17 features and classified using NN and RBFN. The proposed hybrid ADA?NN and ADA?RBFN classifier system shows improvement in classification accuracy over conventional single classifiers. The highest classification rate achieved is 91.56%. The results indicate that the hybrid classifier approach contributes greatly for the classification of mammogram mass into benign and malignant.

Metrological Characterization of a CADx System for the Classification of Breast Masses in Mammograms

In this paper, we perform the assessment of a CAD for the tumoral masses classification in mammograms by the uncertainty propagation through the system. Carrying on the work of the authors concerning the metrological characterization of the developed CAD, we validate the features extraction, features selection, and classification steps in this paper. In particular, suitable metrics such as the Receiving Operating Curve (ROC) and the Area Under ROC (AUC) are widely used in order to provide a quantitative evaluation of the performance. Finally, we implement a Monte Carlo simulation in order to provide the confidence interval for some coverage probabilities for all involved parameters. The procedure is tested on mammographic images containing both malignant and benign breast masses.

Classification of Breast Masses in Mammograms Using Radial Basis Functions and Simulated Annealing

We present pattern classification methods based upon nonlinear and combinational optimization techniques, specifically, radial basis functions (RBF) and simulated annealing (SA), to classify masses in mammograms as malignant or benign. Combinational optimization is used to pre-estimate RBF parameters, namely, the centers and spread matrix. The classifier was trained and tested, using the leave-one-out procedure, with shape, texture, and edge-sharpness measures extracted from 57 regions of interest (20 related to malignant tumors and 37 related to benign masses) manually delineated on mammograms by a radiologist. The classifier’s performance, with preestimation of the parameters, was evaluated in terms of the area Az under the receiver operating characteristics curve. Values up to Az = 0.9997 were obtained with RBF-SA with pre-estimation of the centers and spread matrix, which are better than the results obtained with pre-estimation of only the RBF centers, which were up to 0.9470. Overall, the results with the RBF-SA method were better than those provided by standard multilayer perceptron neural networks.

Toward breast cancer diagnosis based on automated segmentation of masses in mammograms

This work explores the use of characterization features extracted based on breast-mass contours obtained by automated segmentation methods, for the classification of masses in mammograms according to their diagnosis (benign or malignant). Two sets of mass contours were obtained via two segmentation methods (a dynamic-programming-based method and a constrained region-growing method), and simplified versions of these contours (modeling the contours as ellipses) were employed to extract a set of six features designed for characterization of mass margins (contrast between foreground region and background region, coefficient of variation of edge strength, two measures of the fuzziness of mass margins, a measure of spiculation based on relative gradient orientation, and a measure of spiculation based on edge-signature information). Three popular classifiers (Bayesian classifier, Fisher's linear discriminant, and a support vector machine) were then used to predict the diagnosis of a set of 349 masses based on each of said features and some combinations of these. The systems (each system consists of a segmentation method, a featureset, and a classifier) were compared with each other in terms of their performance on the diagnosis of the set of breast masses. It was found that, although there was a percent difference of about 14% in the average segmentation quality between methods, this was translated into an average percent difference of only 4% in the classification performance. It was also observed that the spiculation feature based on edge-signature information was distinctly better than the rest of the features, although it is not very robust to changes in the quality of the segmentation. All systems were more efficient in predicting the diagnosis of benign masses than that of the malignant masses, resulting in low sensitivity and high specificity values (e.g. 0.6 and 0.8, respectively) since the positive class in the classification experiments is the set of malignant masses. It was concluded that features extracted from automated contours can contribute to the diagnosis of breast masses in screening programs by correctly identifying a majority of benign masses.

Errata: Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.

Classification of breast masses in mammograms using genetic programming and feature selection

Mammography is a widely used screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. A small dataset of 57 breast mass images, each with 22 features computed, was used in this investigation; the same dataset has been previously used in other studies. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of the classification technique called genetic programming (GP), which possesses feature selection implicitly. To refine the pool of features available to the GP classifier, we used feature-selection methods, including the introduction of three statistical measures--Student's t test, Kolmogorov-Smirnov test, and Kullback-Leibler divergence. Both the training and test accuracies obtained were high: above 99.5% for training and typically above 98% for test experiments. A leave-one-out experiment showed 97.3% success in the classification of benign masses and 95.0% success in the classification of malignant tumors. A shape feature known as fractional concavity was found to be the most important among those tested, since it was automatically selected by the GP classifier in almost every experiment.

Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features

We propose an approach using artificial neural networks to classify masses in mammograms as malignant or benign. Single-layer and multilayer perceptron networks are used in a study on perceptron topologies and training procedures for pattern classification of breast masses. The contours of a set of 111 regions on mammograms related to breast masses and tumors are manually delineated and represented by polygonal models for shape analysis. Ribbons of pixels are extracted around the boundaries of a subset of 57 masses by dilating and eroding the contours. Three shape factors, three measures of edge sharpness, and 14 texture features based on gray-level co-occurrence matrices of the pixels in the ribbons are computed. Several combinations of the features are used with perceptrons of varying topology and training procedures for the classification of benign masses and malignant tumors. The results are compared in terms of the area Az under the receiver operating characteristics curve. Values of Az up to 0.99 are obtained with the shape factors and texture features. However, only feature sets that included at least one shape factor provide consistently high performance with respect to variations in network topology and training.

Approaches for automated detection and classification of masses in mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The estimated sensitivity of radiologists in breast cancer screening is only about 75%, but the performance would be improved if they were prompted with the possible locations of abnormalities. Breast cancer CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This paper discusses the methods for mass detection and classification, and compares their advantages and drawbacks.

Pattern classification of breast masses via fractal analysis of their contours

We propose methods for the analysis and classification of breast masses in mammograms via fractal analysis of signatures of their contours. Fractal dimension, on its own or in conjunction with other shape factors such as fractional concavity and spiculation index, resulted in the classification of mammographic masses as benign or malignant with overall accuracies of up to 0.93 with a database of 111 contours, as indicated by the area under the receiver operating characteristics curve. Furthermore, fractal dimension was able to classify atypical spiculated benign masses more accurately than other shape factors.

Development of an Automated Method for Classification of Masses in Mammogram

Development of an Automated Method for Classification of Masses in Mammogram