Computer-Based Identification of Breast Cancer Using Digitized Mammograms

Acharya, U. Rajendra; Ng, E. Y. K.; Chang, Yongjun; Yang, Jyh Her; Kaw, Gregory

doi:10.1007/s10916-008-9156-6

Cited by 22 publications

(11 citation statements)

References 16 publications

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“…Instead, a multiparametric approach requires a classification algorithm to combine the parameters into a single parametric map. Many algorithms have been used in biomedicine for classification; in particular, Gaussian mixture models (GMMs), support vector machines and artificial neural networks have been extensively employed [43–45]. GMMs have been chosen for our multiparametric evaluation as these are fast, purely based on data (no need for additional physical modelling) and facilitate the definition of classification confidence.…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

et al. 2016

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ObjectivesThe aim of this study is to improve the accuracy of dynamic contrast-enhanced ultrasound (DCE-US) for prostate cancer (PCa) localization by means of a multiparametric approach.Materials and MethodsThirteen different parameters related to either perfusion or dispersion were extracted pixel-by-pixel from 45 DCE-US recordings in 19 patients referred for radical prostatectomy. Multiparametric maps were retrospectively produced using a Gaussian mixture model algorithm. These were subsequently evaluated on their pixel-wise performance in classifying 43 benign and 42 malignant histopathologically confirmed regions of interest, using a prostate-based leave-one-out procedure.ResultsThe combination of the spatiotemporal correlation (r), mean transit time (μ), curve skewness (κ), and peak time (PT) yielded an accuracy of 81% ± 11%, which was higher than the best performing single parameters: r (73%), μ (72%), and wash-in time (72%). The negative predictive value increased to 83% ± 16% from 70%, 69% and 67%, respectively. Pixel inclusion based on the confidence level boosted these measures to 90% with half of the pixels excluded, but without disregarding any prostate or region.ConclusionsOur results suggest multiparametric DCE-US analysis might be a useful diagnostic tool for PCa, possibly supporting future targeting of biopsies or therapy. Application in other types of cancer can also be foreseen.Key points• DCE-US can be used to extract both perfusion and dispersion-related parameters.• Multiparametric DCE-US performs better in detecting PCa than single-parametric DCE-US.• Multiparametric DCE-US might become a useful tool for PCa localization.

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Section: Introductionmentioning

confidence: 99%

“…GMMs have been chosen for our multiparametric evaluation as these are fast, purely based on data (no need for additional physical modelling) and facilitate the definition of classification confidence. Moreover, GMMs were reported to perform better than neural networks in mammographic tumour identification [45]. …”

Section: Introductionmentioning

confidence: 99%

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

et al. 2016

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“…Several studies regarding mammographic mass detection and classification were introduced recently (Andre & Rangayyan, 2003;Youssry et al, 2003;Chen & Chang, 2004;Hwang et al, 2005;Binh & Thanh, 2007;Acharya et al, 2008;Dominguez & Nandi, 2008;Karahaliou et al, 2008;Lladó et al, 2009;Zeng & Liu, 2010;Liu et al, 2011). However, there are fewer studies related to curvelet and spherical wavelet transform (SWT) than those related to discrete wavelet transform (DWT).…”

Section: Introductionmentioning

confidence: 99%

Computer‐aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines

2014

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Breast cancer can be effectively detected and diagnosed using the technology of digital mammography. However, although this technology has been rapidly developing recently, suspicious regions cannot be detected in some cases by radiologists, because of the noise or inappropriate mammogram contrast. This study presents a classification of segmented region of interests (ROIs) as either benign or malignant to serve as a second eye of the radiologists. Our study consists of three steps. In the first step, spherical wavelet transform (SWT) is applied to the original ROIs. In the second step, shape, boundary and grey level based features of wavelet (detail) and scaling (approximation) coefficients are extracted. Finally, in the third step, malignant/benign classification of the masses is implemented by giving the feature matrices to a support vector machine system. The proposed system achieves 91.4% and 90.1% classification accuracy using the dataset acquired from the hospital of Istanbul University in Turkey and the free Mammographic Image Analysis Society, respectively. Furthermore, discrete wavelet transform, which produces 83.3% classification accuracy, is applied to the coefficients to make a comparison with the SWT method.

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“…Some scholars applied data mining techniques to predict diagnossis for digital mammography [17,18]. Data mining techniques offer precise, accurate, and fast algorithms for such classification using dimensionality reduction, feature extraction, and classification routines.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods

Luo

Cheng

2010

J Med Syst

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Methods that can accurately predict breast cancer are greatly needed and good prediction techniques can help to predict breast cancer more accurately. In this study, we used two feature selection methods, forward selection (FS) and backward selection (BS), to remove irrelevant features for improving the results of breast cancer prediction. The results show that feature reduction is useful for improving the predictive accuracy and density is irrelevant feature in the dataset where the data had been identified on full field digital mammograms collected at the Institute of Radiology of the University of Erlangen-Nuremberg between 2003 and 2006. In addition, decision tree (DT), support vector machine-sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic problem in an attempt to predict results with better performance. The results demonstrate that ensemble classifiers are more accurate than a single classifier.

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Computer-Based Identification of Breast Cancer Using Digitized Mammograms

Cited by 22 publications

References 16 publications

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

Computer‐aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines

Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods

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