False-positive reduction technique for detection of masses on digital mammograms: Global and local multiresolution texture analysis

Wei, Datong; Chan, Heang Ping; Petrick, Nicholas; Sahiner, Berkman; Helvie, Mark A.; Adler, Dorit D.; Goodsitt, Mitchell M.

doi:10.1118/1.598011

Cited by 62 publications

(62 citation statements)

References 22 publications

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“…The morphological features describe the contrast, size, and shape of the object on the PV. Multiresolution global and local texture features are derived from the SGLD matrices, 21 including 364 global ͑13 SGLD texture measuresϫ 14 distances ϫ 2 directions from the entire ROI͒ and 208 local ͑13 SGLD texture measuresϫ 4 distancesϫ 2 directions= 104 features from the object region and 104 from the peripheral background region in the ROI͒. The details of the morphological and texture feature extraction have been described in the literature.…”

Section: Iic Cad System For Projection View Imagesmentioning

confidence: 99%

“…Since we are interested in detecting all types of masses using the mass detection scheme and many malignant masses are not spiculated, a second type of texture features are extracted from the spatial gray level dependence ͑SGLD͒ matrices. 20,21 Thirteen SGLD texture measures are extracted from the 256ϫ 256-pixel region of interest ͑ROI͒ containing the object on each slice at 1 pixel distance and two directions. The same texture features from the different DBT slices are again averaged to obtain one average feature.…”

Section: Iib Cad System For Dbt Volumementioning

confidence: 99%

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Computer‐aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches

et al. 2008

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The authors are developing a computer-aided detection ͑CAD͒ system for masses on digital breast tomosynthesis mammograms ͑DBT͒. Three approaches were evaluated in this study. In the first approach, mass candidate identification and feature analysis are performed in the reconstructed three-dimensional ͑3D͒ DBT volume. A mass likelihood score is estimated for each mass candidate using a linear discriminant analysis ͑LDA͒ classifier. Mass detection is determined by a decision threshold applied to the mass likelihood score. A free response receiver operating characteristic ͑FROC͒ curve that describes the detection sensitivity as a function of the number of false positives ͑FPs͒ per breast is generated by varying the decision threshold over a range. In the second approach, prescreening of mass candidate and feature analysis are first performed on the individual two-dimensional ͑2D͒ projection view ͑PV͒ images. A mass likelihood score is estimated for each mass candidate using an LDA classifier trained for the 2D features. The mass likelihood images derived from the PVs are backprojected to the breast volume to estimate the 3D spatial distribution of the mass likelihood scores. The FROC curve for mass detection can again be generated by varying the decision threshold on the 3D mass likelihood scores merged by backprojection. In the third approach, the mass likelihood scores estimated by the 3D and 2D approaches, described above, at the corresponding 3D location are combined and evaluated using FROC analysis. A data set of 100 DBT cases acquired with a GE prototype system at the Breast Imaging Laboratory in the Massachusetts General Hospital was used for comparison of the three approaches. The LDA classifiers with stepwise feature selection were designed with leave-one-case-out resampling. In FROC analysis, the CAD system for detection in the DBT volume alone achieved test sensitivities of 80% and 90% at average FP rates of 1.94 and 3.40 per breast, respectively. With the 2D detection approach, the FP rates were 2.86 and 4.05 per breast, respectively, at the corresponding sensitivities. In comparison, the average FP rates of the system combining the 3D and 2D information were 1.23 and 2.04 per breast, respectively, at 80% and 90% sensitivities. The difference in the detection performances between the 2D and the 3D approach, and that between the 3D and the combined approach were both statistically significant ͑p = 0.02 and 0.01, respectively͒ as estimated by alternative FROC analysis. The combined system is a promising approach to improving automated mass detection on DBTs.

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Section: Iic Cad System For Projection View Imagesmentioning

confidence: 99%

Section: Iib Cad System For Dbt Volumementioning

confidence: 99%

Computer‐aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches

et al. 2008

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“…The training data set alone was used for training the classification rules and the weights of the LDA classifier. After morphological classification, global and local multi-resolution texture analyses 45 are performed in each remaining ROI by using the spatial gray level dependence (SGLD) matrix. Briefly, the wavelet transform is employed to decompose an ROI into three levels for global texture analysis.…”

Section: B Methodsmentioning

confidence: 99%

Computer‐aided detection of breast masses on full field digital mammograms

et al. 2005

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We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.

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“…The formulation of these texture measures has been described in the literature. 16 Two corresponding features in the diagonal direction (θ =45° and 135°) were averaged to yield a single feature. Similarly, two corresponding features in the horizontal and vertical directions (θ =0° and 90°) were also averaged.…”

Section: Feature Extractionmentioning

confidence: 99%

Joint two‐view information for computerized detection of microcalcifications on mammograms

et al. 2006

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We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Candidate pairs were classified with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 116 pairs of mammograms containing microcalcification clusters and 203 pairs of normal images from the University of South Florida (USF) public database was used for training the two-view detection algorithm. The trained method was tested on an independent test set of 167 pairs of mammograms, which contained 71 normal pairs and 96 pairs with microcalcification clusters collected at the University of Michigan (UM). The similarity classifier had a very low FP rate for the test set at low and medium levels of sensitivity. However, the highest mammogram-based sensitivity that could be reached by the similarity classifier was 69%. The single-view classifier had a higher FP rate compared to the similarity classifier, but it could reach a maximum mammogram-based sensitivity of 93%. The fusion method combined the scores of these two classifiers so that the number of FPs was substantially reduced at relatively low and medium sensitivities, and a relatively high maximum sensitivity was maintained. For the malignant microcalcification clusters, at a mammogram-based sensitivity of 80%, the FP rates were 0.18 and 0.35 with the two-view fusion and single-view detection methods, respectively. When the training and test sets were switched, a similar improvement was obtained, except that both the fusion and single-view detection methods had superior test performances on the USF data set than those on the UM data set. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.

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False-positive reduction technique for detection of masses on digital mammograms: Global and local multiresolution texture analysis

Cited by 62 publications

References 22 publications

Computer‐aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches

Computer‐aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches

Computer‐aided detection of breast masses on full field digital mammograms

Joint two‐view information for computerized detection of microcalcifications on mammograms

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