Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

Wei, Chia-Hung; Gwo, Chih-Ying; Huang, Peisen S.

doi:10.1259/bjr.20150802

Cited by 8 publications

(8 citation statements)

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“…The pectoralis muscle on the MLO view, is detected following a variant of the method described by Wei et al . A rectangular region of interest is generated, starting from the top left corner of the MLO raw edge [which is the (0, 0) coordinate].…”

Section: Methodsmentioning

confidence: 99%

“…The pectoralis muscle on the MLO view, is detected following a variant of the method described by Wei et al 32 A rectangular region of interest is generated, starting from the top left corner of the MLO raw edge [which is the (0, 0) coordinate]. The height is 90% of the maximum height coordinate found on the MLO raw contour, and the width is 95% of the width displayed on the upper part of the MLO view, before the contour starts ascending in height.…”

Section: B5 Pectoralis Detectionmentioning

confidence: 99%

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Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes

et al. 2017

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Purpose To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast. Methods We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm. From these shapes three generic models (CC-only, MLO-only, and joint CC/MLO) with linearly independent components were constructed via principal component analysis (PCA). The ability of the models to represent mammograms not used for PCA was tested via leave-one-out cross-validation, by measuring the average distance error (ADE). Results The individual models based on six components were found to depict breast shapes with accuracy (mean ADE-CC=0.81 mm, ADE-MLO=1.64 mm, ADE-Pectoralis=1.61 mm), outperforming the joint CC/MLO model (p≤0.001). The joint model based on 12 principal components contains 99.5% of the total variance of the data, and can be used to generate new clinically realistic paired CC and MLO breast shapes. This is achieved by generating random sets of 12 principal components, following the Gaussian distributions of the histograms of each component, which were obtained from the component values determined from the images in the mammography database used. Conclusion Our joint CC/MLO model can successfully generate paired CC and MLO view shapes of the same simulated breast, while the individual models can be used to represent with high accuracy clinical acquired mammograms with a small set of parameters. This is the first step towards objective 3D compressed breast models, useful for dosimetry and scatter correction research, among other applications.

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Section: Methodsmentioning

confidence: 99%

Section: B5 Pectoralis Detectionmentioning

confidence: 99%

Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes

et al. 2017

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“…Wei et al [26] mainly aimed to detect the boundary of obscure pectoral muscle in MLO mammograms. They first partitioned the pectoral muscle and then used different threshold values for each partition.…”

Section: (H) (I) (P)mentioning

confidence: 99%

Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours

Ture

Kayıkçıoğlu

2020

IEEE Access

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Automatic detection of the pectoral muscle in mammograms is widely used in computeraided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study,we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.

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“…In recent years, a lot of automatic pectoral muscle removal methods have been developed [ 10 ]. However, due to the variations in size, shapes, intensity, and contrasts of the pectoral muscles, most of the existing techniques [ 8 – 11 ] fail to remove accurate muscle regions from the entire mammograms. The advantages of our proposed method are: 1) muscle detection possibility is improved, even in low contrast problems, 2) pectoral muscle shape tracking is attained without using of the heuristic thresholding, and 3) to identify the boundary of a breast.…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of our proposed method are: 1) muscle detection possibility is improved, even in low contrast problems, 2) pectoral muscle shape tracking is attained without using of the heuristic thresholding, and 3) to identify the boundary of a breast. The existence of these problems may lead to wrong assumptions of a false-(negative and positive) rates with un-desired biopsies [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Removal of pectoral muscle based on topographic map and shape-shifting silhouette

et al. 2018

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BackgroundIn digital mammography, finding accurate breast profile segmentation of women’s mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing.MethodsWe propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images.ResultsTo assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth.ConclusionsOur approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.

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Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

Cited by 8 publications

References 50 publications

Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes

Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes

Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours

Removal of pectoral muscle based on topographic map and shape-shifting silhouette

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