A Simple Method for Automatically Locating the Nipple on Mammograms

Chandrasekhar, Ramachandran; Attikiouzel, Y.

doi:10.1117/3.651880.ch20

Cited by 15 publications

(21 citation statements)

References 3 publications

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“…The breast shape edge was then located using Roberts cross gradient operators and edge thinning. [24][25][26] The breast edge was uniformly sampled to generate a subset of 100 (x,y) coordinate points, in units of distance. This subset of points can be defined as 100 correlated observations, which can be reduced using PCA to a smaller set of linearly independent variables that encapsulates most of the information contained within the datapoints.…”

Section: D Automated Edge Detection and Patient Breast Shape Charamentioning

confidence: 99%

X‐ray scatter correction in breast tomosynthesis with a precomputed scatter map library

et al. 2014

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Purpose: To develop and evaluate the impact on lesion conspicuity of a software-based x-ray scatter correction algorithm for digital breast tomosynthesis (DBT) imaging into which a precomputed library of x-ray scatter maps is incorporated. Methods: A previously developed model of compressed breast shapes undergoing mammography based on principal component analysis (PCA) was used to assemble 540 simulated breast volumes, of different shapes and sizes, undergoing DBT. A Monte Carlo (MC) simulation was used to generate the cranio-caudal (CC) view DBT x-ray scatter maps of these volumes, which were then assembled into a library. This library was incorporated into a previously developed software-based x-ray scatter correction, and the performance of this improved algorithm was evaluated with an observer study of 40 patient cases previously classified as BI-RADS R 4 or 5, evenly divided between mass and microcalcification cases. Observers were presented with both the original images and the scatter corrected (SC) images side by side and asked to indicate their preference, on a scale from −5 to +5, in terms of lesion conspicuity and quality of diagnostic features. Scores were normalized such that a negative score indicates a preference for the original images, and a positive score indicates a preference for the SC images. Results: The scatter map library removes the time-intensive MC simulation from the application of the scatter correction algorithm. While only one in four observers preferred the SC DBT images as a whole (combined mean score = 0.169 ± 0.37, p > 0.39), all observers exhibited a preference for the SC images when the lesion examined was a mass (1.06 ± 0.45, p < 0.0001). When the lesion examined consisted of microcalcification clusters, the observers exhibited a preference for the uncorrected images (−0.725 ± 0.51, p < 0.009). Conclusions:The incorporation of the x-ray scatter map library into the scatter correction algorithm improves the efficiency of the algorithm. The observer study presented here is also the first test of the scatter correction algorithm with patient images and human observers, and demonstrates its potential to improve the clinical performance of DBT.

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Section: D Automated Edge Detection and Patient Breast Shape Charamentioning

confidence: 99%

X‐ray scatter correction in breast tomosynthesis with a precomputed scatter map library

et al. 2014

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“…A low-pass filter can be used to find the local orientation that varies slowly in the local neighborhood. Before performing low-pass filtering, the orientation image was converted into a continuous vector field 13 defined as follows: (7) and (8) The low-pass filtering was performed by averaging of ϴ x (i, j) and ϴ y (i, j) in a local window with a size of 5×5 pixels, yielding , and , respectively, as the smoothed continuous vector field. The smoothed local orientation at (i, j) can then be computed as (9) Figure 5 shows an example of a computed orientation field superimposed on the original mammogram.…”

Section: Nipple Detectionmentioning

confidence: 99%

“…Automated methods for detection of the nipple location have been reported by Chandrasekhar, 7 Mendez, 8 and Yin. 9 In their methods, the breast boundary was extracted and then the nipple location was identified by searching for the maximum and minimum of the gradient changes or average intensity in a small region along the breast boundary.…”

Section: Introductionmentioning

confidence: 99%

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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“…Mammogram segmentation usually involves classifying mammograms into several distinct regions, including the breast border [5], the nipple [6] and the pectoral muscle. The principal feature on a mammogram is the breast border, otherwise known as the skin-air interface, or breast boundary.…”

Section: Mammogram Segmentationmentioning

confidence: 99%

Segmentation of the Breast Region in Digital Mammograms and Detection of Masses

Sahakyan¹,

Sarukhanyan²

2012

IJACSA

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Abstract-The mammography is the most effective procedure for an early diagnosis of the breast cancer. Finding an accurate and efficient breast region segmentation technique still remains a challenging problem in digital mammography. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing algorithm in order to: remove digitization noises and separate background region from the breast profile region for further edge detection and regions segmentation.

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A Simple Method for Automatically Locating the Nipple on Mammograms

Cited by 15 publications

References 3 publications

X‐ray scatter correction in breast tomosynthesis with a precomputed scatter map library

X‐ray scatter correction in breast tomosynthesis with a precomputed scatter map library

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

Segmentation of the Breast Region in Digital Mammograms and Detection of Masses

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