Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

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

doi:10.1118/1.597756

Cited by 113 publications

(67 citation statements)

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“…Petrick et al 9 developed an algorithm for detection of masses by use of a region-growing technique on the objects. The system used a filter for distinction of contrast, which they used in their previous work 7 to enhance structures of mammographic interest. The region-growing-based technique was then applied to each of the identified structures, with gray scales and the gradient used to reduce the overlapping of structures.…”

Section: Introductionmentioning

confidence: 99%

“…The method was tested in a set of five images, yielding a sensitivity of 83% with 0.6 false positive per image. The wavelet transform was applied for analysis of masses, 6,7 and texture features based on the matrices of spatial graylevel dependence (SGLD) were used for each region of interest (ROI) at different scales. The best features were selected by the stepwise method and by minimization of the Mahalanobis 8 published a study on detection of malignant masses in mammograms based on a nonlinear analysis method for multiscales.…”

Section: Introductionmentioning

confidence: 99%

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Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

et al. 2006

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This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

et al. 2006

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“…We used the methods already developed in out lab, which work reliably for segmentation of the breast image from the background for our automated detection algorithms for single images [1], [2].…”

Section: (Task 2)mentioning

confidence: 99%

Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection

Hadjiiski¹

2004

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“…34 We have developed a CAD system for the detection of masses on SFMs in our previous studies. 30,35,36 We are developing a mass detection system for mammograms acquired directly by a FFDM system. In this study, we adapted our mass detection system developed for SFMs to FFDMs by optimizing each stage and retraining.…”

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confidence: 99%

“…Their approaches to prescreening of mass candidates were based primarily on mass characteristics including: (1) asymmetric density between left and right mammograms, [19][20][21][22] (2) texture, 23,24 (3) spiculation, 25,26 (4) gray level contrast, [27][28][29][30][31] and (5) gradient. 32 Some of these approaches were refined with a combination of the mass characteristics.…”

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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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Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

Cited by 113 publications

References 0 publications

Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection

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

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