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Breast masses in mammography classification with local contour features

View Article: PubMed Central - PubMed

ABSTRACT

Background: Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.

Methods: In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.

Results: The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.

Conclusion: The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Electronic supplementary material: The online version of this article (doi:10.1186/s12938-017-0332-0) contains supplementary material, which is available to authorized users.

No MeSH data available.


The bar chart of accuracy for four features used KNN, SVM and ANN classifier
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Fig7: The bar chart of accuracy for four features used KNN, SVM and ANN classifier

Mentions: Figure 7 and Table 1 show the performance of four features with three different classifiers. No matter which classifier is used, the result proves that our proposed feature is better than existing one. To the features and , SVM classifier is the almost the same as ANN and is better than KNN. To other features, SVM is the best among these three classifiers. SVM is robust for small sample data. The accuracy of fractal feature is 99.33%. Its performance is better than and . This is because the 1D signature of contour for breast mass accords with the fractal characteristic. The highest accuracy is 99.96% using the feature of root mean square slope with SVM classifier. The reason is that RMS slope can describe the variation of vertical direction of 1D signature. It is very important to distinguish the benign mass and malignant one.Fig. 7


Breast masses in mammography classification with local contour features
The bar chart of accuracy for four features used KNN, SVM and ANN classifier
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5391548&req=5

Fig7: The bar chart of accuracy for four features used KNN, SVM and ANN classifier
Mentions: Figure 7 and Table 1 show the performance of four features with three different classifiers. No matter which classifier is used, the result proves that our proposed feature is better than existing one. To the features and , SVM classifier is the almost the same as ANN and is better than KNN. To other features, SVM is the best among these three classifiers. SVM is robust for small sample data. The accuracy of fractal feature is 99.33%. Its performance is better than and . This is because the 1D signature of contour for breast mass accords with the fractal characteristic. The highest accuracy is 99.96% using the feature of root mean square slope with SVM classifier. The reason is that RMS slope can describe the variation of vertical direction of 1D signature. It is very important to distinguish the benign mass and malignant one.Fig. 7

View Article: PubMed Central - PubMed

ABSTRACT

Background: Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.

Methods: In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.

Results: The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.

Conclusion: The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Electronic supplementary material: The online version of this article (doi:10.1186/s12938-017-0332-0) contains supplementary material, which is available to authorized users.

No MeSH data available.