Contourlet-based mammography mass classification using the SVM family

Moayedi, Fatemeh; Azimifar, Zohreh; Boostani, Reza; Katebi, S. D.

doi:10.1016/j.compbiomed.2009.12.006

Cited by 109 publications

(46 citation statements)

References 14 publications

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“…A survey of studies utilizing statistical methods exhibits that graph-based approaches have recently been used for pectoral muscle removal [24][25][26]. Li et al and Liu et al modeled a pectoral muscle region as a variable with normal distribution on the basis that a pectoral muscle is more uniform than a breast parenchyma [27,28]. Liu et al performed pectoral muscle removal by computing the probability of each pixel to be in the high-frequency region [27].…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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“…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]. Moayedi et al [12] employed the contourlet transform to obtain the contourlet coefficient as a feature and Nascimento et al [10] extracted multiresolution analysis features by using wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, several methods using multiresolution analysis have been proposed for feature extraction in mammograms [10,12]. Moayedi et al [12] employed the contourlet transform to obtain the contourlet coefficient as a feature and Nascimento et al [10] extracted multiresolution analysis features by using wavelet transform. In general, the feature space is large, complex, and with redundancy that the excessive features may decrease the classification accuracy and waste time.…”

Section: Introductionmentioning

confidence: 99%

“…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 %.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the redundant features should be removed to improve the performance of the algorithm. While it is difficult to select an optimized combination of features from the whole features, the genetic algorithm (GA) and stepwise method are the most frequently used method to select feature [6,12].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

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Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Kashyap

Bajpai

Khanna

et al. 2017

Numer Methods Biomed Eng

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Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.

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Contourlet-based mammography mass classification using the SVM family

Cited by 109 publications

References 14 publications

A novel multistage system for the detection and removal of pectoral muscles in mammograms

A novel multistage system for the detection and removal of pectoral muscles in mammograms

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

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