An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong, Min; Lǚ, Xiangyu; Ma, Yide; Guo, Yanan; Ma, Yurun; Wang, Keju

doi:10.1007/s10278-015-9778-4

Cited by 82 publications

(43 citation statements)

References 33 publications

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“…This proposed approach yielded promising results when evaluating on 70 mass mammograms from mini-MIAS database. Dong et al [16] presented a novel automatic segmentation and classification base on DDSM and MIAS database, the experimental results verified the effectiveness of this new approach.…”

Section: Introductionmentioning

confidence: 87%

“…In the previous study, MIAS [31] was widely used in mammography analysis because that they are freely available [13,16,32,33]. In this work, we choose the same dataset, the same as other researchers.…”

Section: Databasementioning

confidence: 99%

“…These classification techniques can be divided into two categories. One is image analysis with segmentation the lesion areas [10][11][12][13][14][15][16], and the other is image analysis without segmentation [17][18][19][20][21][22][23]. Wei et al [10] come up with a content-based mammogram retrieval system; meanwhile, a similarity measure scheme was proposed, this study was tested on Digital Database for Screening Mammography (DDSM) dataset, and experimental results demonstrated that round-shape masses were most discriminative when using Zernike moments and roundshape, circumscribed margin masses could achieve the highest precision among all mass types.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

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Section: Introductionmentioning

confidence: 87%

Section: Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

Self Cite

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“…Mammography systems for the computer-aided detection (CAD) of cancer masses perform the following steps: preprocessing [1][2][3], segmentation [4][5][6][7], feature extraction [8][9][10] and classification [11][12][13]. However, whether a CAD system will be successfully adopted in clinical practice depends mainly on the segmentation algorithm or algorithms used.…”

Section: Introductionmentioning

confidence: 99%

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Ciecholewski

2017

Symmetry

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Abstract:The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to segment the masses: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), a geometric active contour model (GAC) and an active contour without edges (ACWE). The preprocessing techniques presented in this publication are to reduce noise and at the same time amplify uniform areas of images in order to improve segmentation results. In addition, the use of image sampling by bicubic interpolation was tested to shorten the evolution time of active contour methods. The experiments used a test set composed of 100 cases taken from two publicly available databases: Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) database. The qualitative assessment concerned the ability to formulate an adequate diagnostic hypothesis and, for the individual methods (malignant and benign cases together), it amounted to at least: 81% (EM), 76% (GAC), and 69% (ACWE).

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“…More recently, with the emergence and growth of digital mammography technology in parallel with spread of computerized algorithms for image interpretation and diagnosis [29], interest in measuring breast density in a fully automatic, quantitative, as well as volumetric manner has grown [18,[30][31][32][33]. Noticeably, due to the rise in information processing power and growing amounts of data being created, machine learning algorithms have also seen an increased presence in such computer-aided analyses in breast imaging and radiology as a whole [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

et al. 2016

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Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and interpatient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

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An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Cited by 82 publications

References 33 publications

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

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