Classification of compressed breast shapes for the design of equalization filters in x‐ray mammography

Goodsitt, Mitchell M.; Chan, Heang Ping; Liu, Bob; Guru, S.V.; Morton, Amy; Keshavmurthy, Shyam P.; Petrick, Nick

doi:10.1118/1.598272

Cited by 35 publications

(26 citation statements)

References 14 publications

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“…The breast boundary was first identified by a boundary tracking technique. The automated boundary tracking technique previously developed 10,11 was modified to improve its performance. The breast boundary was identified by a gradient-based method as follows.…”

Section: B Breast Boundary Detectionmentioning

confidence: 99%

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Computerized nipple identification for multiple image analysis in computer‐aided diagnosis

et al. 2004

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Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A twostage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations which were used as the gold standard. In the training data set, 301 nipples were positively identified and were referred to as visible nipples. Seventy six nipples could not be positively identified and were referred to as invisible nipples. The radiologists provided their estimation of the nipple locations in the latter group for comparison with the computer estimates. The computerized method could detect 89.37% (269/301) of the visible nipples and 69.74% (53/76) of the invisible nipples within 1 cm of the gold standard. In the test data set, 298 and 69 of the nipples were classified as visible and invisible, respectively. 92.28% (275/298) of the visible nipples and 53.62% (37/69) of the invisible nipples were identified within 1 cm of the gold standard. The results demonstrate that the nipple locations on digitized mammograms can be accurately detected if they are visible and can be reasonably estimated if they are invisible. Automated nipple detection will be an important step towards multiple image analysis for CAD.

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Section: B Breast Boundary Detectionmentioning

confidence: 99%

“…(1) Convert the smoothed orientation image into a continuous vector field: 14 (10) (2) Identify the points of O x in the inner profile region and then average to a 1D profile: (11) where n I is the number of points within the local window represented by R I . In our study, the size of R I is 5 35.…”

Section: Nipple Detectionmentioning

confidence: 99%

Computerized nipple identification for multiple image analysis in computer‐aided diagnosis

et al. 2004

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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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“…Their method provides around 94% acceptable results from 300 images from MIAS [27] mammogram database. A quadratic/cubic polynomial fitting method was proposed by Goodsitt et al [17] which is fitted by translation and rotation the axes. -Active Contours based techniques.…”

Section: -Polynomial Modelling Based Techniques An Early Methods Propmentioning

confidence: 99%

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

Raba

Oliver

Martı́

et al. 2005

Pattern Recognition and Image Analysis

125

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Abstract. Previous works on breast tissue identification and abnormalities detection notice that the feature extraction process is affected if the region processed is not well focused. Thereby, it is important to split the mammogram into interesting regions to achieve optimal breast parenchyma measurements, breast registration or to put into focus a technique when we search for abnormalities. In this paper, we review most of the relevant work that has been presented from 80's to nowadays. Secondly, an automated technique for segmenting a digital mammogram into breast region and background, with pectoral muscle suppression is presented.

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Classification of compressed breast shapes for the design of equalization filters in x‐ray mammography

Cited by 35 publications

References 14 publications

Computerized nipple identification for multiple image analysis in computer‐aided diagnosis

Computerized nipple identification for multiple image analysis in computer‐aided diagnosis

Objective models of compressed breast shapes undergoing mammography

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

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