Model-Based Characterization of Mammographic Masses

Heidt, Sven-René von der; Elter, Matthias; Wittenberg, Thomas; Paulus, Dietrich

doi:10.1007/978-3-540-93860-6_58

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The performance levels of these lesion based schemes vary widely due to the use of different image processing algorithms, feature classifiers, and the training/testing datasets. For example, the reported performance levels ranged from AUC=0.6 (Heidt et al 2009), AUC=0.81 (Varela et al 2006), AUC=0.84 (Shi et al 2008), to AUC=0.94 (Guliato et al 2008). However, our new CAD scheme is not another lesion-based classification scheme.…”

Section: Discussionmentioning

confidence: 94%

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: Discussionmentioning

confidence: 94%

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

Tan¹,

Pu²

2014

Phys. Med. Biol.

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Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies

Horsch

Hapfelmeier²,

Elter

2011

Int J CARS

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Overall, both the use of the DDSM as data source for evaluation of mammography CAD systems, and the application of statistical evaluation methods were found highly diverse. Results reported from different studies are therefore hardly comparable. Drawbacks of the DDSM (e.g. varying quality of lesion annotations) may contribute to the reasons. But larger bias seems to be caused by authors' own decisions upon study design. RECOMMENDATIONS/CONCLUSION: For future evaluation studies, we derive a set of 13 recommendations concerning the construction and usage of a test database, as well as the application of statistical evaluation methods.

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Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

Tan

Zheng

2014

Int J CARS

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Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice.

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Model-Based Characterization of Mammographic Masses

Cited by 3 publications

References 10 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

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

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