Identification of the breast boundary in mammograms using active contour models

Ferrari, Ricardo José; Rangayyan, Rangaraj M.; Desautels, J. E. Leo; Borges, R. A.; Frere, A.F.

doi:10.1007/bf02344632

Cited by 93 publications

(70 citation statements)

References 13 publications

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“…There have been several other methods proposed to automatically segment or detect the breast boundaries in mammograms. [44][45][46][47] Limitations of this study include the lack of including an automated nipple detection and removal algorithm, so that the model of the breast shape would not be affected by its presence. In addition, inclusion of an automated pectoralis detection algorithm, especially in the MLO view, such as that proposed by Kwok et al, 48 and its inclusion as another edge in the (x,y) coordinate pair vector for the PCA could allow for this image feature to also be included.…”

Section: Discussionmentioning

confidence: 99%

Objective models of compressed breast shapes undergoing mammography

Feng¹,

Patel²,

Sechopoulos³

2013

Medical Physics

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Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). Results: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. Conclusions: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

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Section: Discussionmentioning

confidence: 99%

Objective models of compressed breast shapes undergoing mammography

Feng¹,

Patel²,

Sechopoulos³

2013

Medical Physics

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“…A method for breast boundary identification in mammograms using active contour models is developed by Ferrari et al [14] that can be used for the preprocessing of mammogram. Fu et al [17] proposed a two-stage procedure to detect microcalcifications.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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“…In addition, the markers and labels in the image background may be connected to the breast if they are placed too close to the breast or if the breast is large. Most breast boundary detection algorithms 9,[15][16][17][18][19][20] share a common approach, in which an initial breast area is obtained by analyzing the gray level histogram and the final breast boundary is determined by a subsequent refinement procedure. These algorithms may fail if the initial breast boundary ͑MTBB-Initial͒ deviates too far from the true boundary such that the refinement procedure is misled to the wrong direction.…”

Section: Introductionmentioning

confidence: 99%

“…After performing edge enhancement and noise removal, those control points were input to the snake to obtain the final breast boundary. Ferrari et al 18 determined the initial breast boundary based on the following three steps. First, the image contrast was enhanced using the logarithmic operator.…”

Section: Introductionmentioning

confidence: 99%

Dynamic multiple thresholding breast boundary detection algorithm for mammograms

Wu¹,

Zhou²,

Chan³

et al. 2009

Medical Physics

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Purpose: Automated detection of breast boundary is one of the fundamental steps for computeraided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary ͑MTBB͒ detection method for digitized mammograms. Methods: A large data set of 716 screen-film mammograms ͑442 CC view and 274 MLO view͒ obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary ͑MTBB-Initial͒ was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary ͑MTBB-Final͒. The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance ͑HDist͒, the average minimum Euclidean distance ͑AMinDist͒, and the area overlap measure ͑AOM͒. Results: In comparison with the authors' previously developed gradient-based breast boundary ͑GBB͒ algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels ͑4.8 mm͒ for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels ͑1.2 mm͒ for GBB, MTBB-Initial, and MTBBFinal, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test ͑p Ͻ 0.0001͒. Conclusions:The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.

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Identification of the breast boundary in mammograms using active contour models

Cited by 93 publications

References 13 publications

Objective models of compressed breast shapes undergoing mammography

Objective models of compressed breast shapes undergoing mammography

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

Dynamic multiple thresholding breast boundary detection algorithm for mammograms

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