Automated breast tissue density assessment using high order regional texture descriptors in mammography

Law, Yan Nei; Lieng, Monica K.; Li, Jingmei; Khoo, David Aik-Aun

doi:10.1117/12.2043332

Cited by 4 publications

(9 citation statements)

References 13 publications

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“…Then, the pectoral muscle is detected by employing linear regression to fit all the identified pixels with the maximum gradient on a straight line/fit. Finally, all the pixels within the pectoral muscle are discarded from the segmented breast area as these pixels do not add useful/beneficial information for the classification task (Law et al , 2014). …”

Section: Methodsmentioning

confidence: 99%

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

Tan¹,

Pu²

2014

Phys. Med. Biol.

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The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were provided to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC=0.793±0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

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Section: Methodsmentioning

confidence: 99%

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

Tan¹,

Pu²

2014

Phys. Med. Biol.

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“…Some previous studies showed that extracting density features from the dense region 30 or retroareolar region 45 led to better prediction accuracy. In our study, the breast area was divided into five subregions according to intensity of each pixel, and all the density and texture features were computed within different subregions.…”

Section: Discussionmentioning

confidence: 99%

“…23,31,32 Once the breast region was identified, we performed a kclass fuzzy c means (FCM) clustering to partite it into several subregions and each one has relatively homogeneous density values. Instead of stratifying breast areas into dense and fatty regions only, 30,[33][34][35] more subregions were extracted and analyzed from the original breast depicted on mammogram. In this study, the number of clusters k was set to 5, so every breast region was separated into five areas.…”

Section: B Mammographic Subregions Segmentationmentioning

confidence: 99%

“…[27][28][29] Furthermore, we noticed that in most reported studies, computerized features were measured and extracted from the single region of each mammogram, usually the whole breast area, but only a few considered the internal tissue/structural information. 30 The purpose of this study is to develop a new framework for near-term breast cancer risk analysis for multiscale texture measurements on mammograms as well as more accurate and sensitive measurements of breast density. In our previous research, we have designed and implemented a prototype of breast cancer risk assessment approach using mammographic density information.…”

Section: Introductionmentioning

confidence: 99%

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Using multiscale texture and density features for near-term breast cancer risk analysis

et al. 2015

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Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions:The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. C 2015 American Association of Physicists in Medicine. [http://dx

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“…Breast cancer is the most prevailing source of cancer-related deaths among women across the globe. Yearly, there are approximately 450, 000 deaths, out of which, breast cancer accounts for about 14% of all female cancer deaths ( [24]). Recent statistics says that 1 out of 10 women is affected by breast cancer in their lifetime.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Method to Localize Masses in Mammograms

2021

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Breast cancer is one of the most common and prevalent type of cancer that mainly affects the women population. chances of effective treatment increase with early diagnosis. Mammography is considered one of the effective and proven technique for early diagnosis of breast cancer. Tissues around masses look identical in mammogram, which makes automatic detection process a very challenging task; they are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area followed by extraction of features to reject false detection. We applied our method on two popular publicly available data-sets (mini-MIAS and DDSM). A total of 56 images from minimias database, and 76 images from DDSM were randomly selected. Results are explained in-terms of ROC (Receiver Operating Characteristics) curves and compared with other state of the art techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.

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Automated breast tissue density assessment using high order regional texture descriptors in mammography

Cited by 4 publications

References 13 publications

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

Using multiscale texture and density features for near-term breast cancer risk analysis

Unsupervised Method to Localize Masses in Mammograms

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