Classification of benign and malignant breast tumors on the basis of 36 radiographic properties

Ackerman, L V; Mucciardi, Anthony N.; Gose, Earl E.

doi:10.1002/1097-0142(197302)31:2<342::aid-cncr2820310212>3.0.co;2-i

Cited by 31 publications

(11 citation statements)

References 4 publications

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“…Computerized classification of mammographic lesions using radiologist-extracted features has also been reported by a number of investigators. Ackerman et al 24 estimated the probability of malignancy of mammographic lesions by analyzing 36 radiologist-extracted characteristics with an automatic clustering algorithm and obtained a specificity of 45% at a sensitivity of 100% in a data set of 102 cases. Gale et al 25 analyzed 12 radiologist-extracted features of mammographic lesions with a computer algorithm and obtained a specificity of 88% at a sensitivity of 79% in a data base of 500 patients.…”

Section: Introductionmentioning

confidence: 99%

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

et al. 1998

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We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.

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Section: Introductionmentioning

confidence: 99%

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

et al. 1998

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“…This way, we will obtain for the Jeffries-Matusita distance: JT= {2[1-exp(-JB)1}112 (4) Where (5) B Therefore, the maximum value of Jeffries-Matusita distance will be . This means that when the distance reaches that value, we have a maximum separation among the classes [27].…”

Section: Shape and Texture Featuresmentioning

confidence: 99%

“…These works present an investigation of shape and/or texture features in the characterization ofbreast masses. Ackerman and Gose [4] employed four features to classify benign and malignant lesions (calcification, spiculation, roughness, and shape). They used Bayesian classifier to characterize texture features in xeromammograms.…”

Section: Introductionmentioning

confidence: 99%

<title>Comparative of shape and texture features in classifications of breast masses in digitized mammograms</title>

et al. 2000

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The aim of this work was to determine a methodology to selection of the best features subset and artificial neural network (ANN) topology to classify masses lesions. The backpropagation training algorithm was used to adjust the weights of ANN. A total of 118 regions of interest images were chosen (68 benign and 50 malignant lesions). In a first step, images were submitted to a combined process of thresholding, mathematical morphology, and region growing techniques. After, fourteen texture features (Haralick descriptors) and fourteen shape features (circularity, compactness, Gupta descriptors, Shen descriptors, Hu descriptors, Fourier descriptor and Wee descriptors) were extracted. The Jeffries-Matusita method was used to select the best features. Three shape features sets and three texture features sets were selected. The Receiver Operating Characteristic (ROC) analyses were conducted to evaluated the classifier performance. The best result for shape features set was accurate classification rate of 98.21%, specificity of 98.37%, sensitivity of 98.00% and the area under ROC curve of 0.99, for a ANN with 5 hidden units. The best result for texture feature set was accurate classification rate of 97.08%, specificity of98.53%, sensitivity of95.l 1% and the area under ROC curve of0.98, for an ANN with 4 hidden units.

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“…The majority of these studies have followed two approaches. The first approach is based on computer extracted morphology/shape features of individual MCCs [5][6], since morphology is one of the most important clinical factors in breast cancer diagnosis. CAD schemes that employ the radiologists' ratings of MCCs morphology have also been proposed [6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is based on computer extracted morphology/shape features of individual MCCs [5][6], since morphology is one of the most important clinical factors in breast cancer diagnosis. CAD schemes that employ the radiologists' ratings of MCCs morphology have also been proposed [6][7]. The second approach employs texture features extracted from ROIs containing MCCs [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Design of a High-Accuracy Classifier Based on Fisher Discriminant Analysis: Application to Computer-Aided Diagnosis of Microcalcifications

Hamdi

Auhmani

Hassani

2008

2008 International Conference on Computational Sciences and Its Applications

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In this paper we present a high accuracy computer-aided diagnosis scheme. The goal of the developed system is to classify benign and malignant microcalcifications on mammograms. It is mainly based on a combination of wavelet decomposition, feature extraction and classification methodology using Fisher's linear discriminant. The contribution of wavelet decomposition is to denoise and to enhance regions of interests (ROI) containing abnormalities. Feature extraction is performed using spatial grey level dependence (SGLD) matrices. The purpose of classification is to assign an object to a certain class. Many classification methods have been described. Here we use Fisher's linear discriminant. Fisher's linear discriminant is particularly useful for discriminating between two classes in a multidimensional space. Since it is based only on the first and second moments of each distribution, it is not a computationally intensive method. Our results show that the developed method is effective for quantifying the classification of benign and malignant microcalcifications abnormalities with an accuracy of 95.5%.

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Classification of benign and malignant breast tumors on the basis of 36 radiographic properties

Cited by 31 publications

References 4 publications

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

<title>Comparative of shape and texture features in classifications of breast masses in digitized mammograms</title>

Design of a High-Accuracy Classifier Based on Fisher Discriminant Analysis: Application to Computer-Aided Diagnosis of Microcalcifications

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