Automatic extraction of pectoral muscle in the MLO view of mammograms

Feudjio, Cyrille; Klein, John; Tiedeu, Alain; Colot, Olivier

doi:10.1088/0031-9155/58/23/8493

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…In another study, Moayedi et al used the logarithm of the pixel energies for pectoral muscle removal [28]. Mean shift segmentation [29], connected component labeling [30], RGM [31][32][33][34], RGM combined with geometric rules [35], and fuzzy c-means clustering [36] are some examples of other methods used for pectoral muscle removal in the literature.…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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“…Volpara’s™ machine learning approach additionally utilizes image normalization, algorithmic padding, image sizing and contrast adjustment, and altering image resolution to improve algorithm efficiency [33]. Fuzzy C-Means clustering algorithms can be utilized to acquire the pectoral muscle region of the breast, while the final pectoral muscle contour is acquired through iterative contour improvement and validation [32].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methodologies are also u4lized for the segmenta4on of the pectoral muscle from mammograms [26,[32][33][34]. An example of this is seen through a connected component labeling method to remove the pectoral muscle region of the breast from the mammogram.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Automatic Pectoral Muscle Segmentation for Mammograms

White,

Harrow,

Cinelli

et al. 2024

Preprint

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The computational analysis to assist radiologists in the interpretation of mammograms usually requires a pre-processing step where the image is converted into a black and white mask to separate breast tissue from the pectoral muscle and the image background. The manual delineation of the breast tissue from the mammogram image is subjective and time-consuming. The 2D Wavelet Transform Modulus Maxima (WTMM) segmentation method, a powerful and versatile multi-scale edge detection approach, is adapted and presented as a novel automated breast tissue segmentation method. The algorithm computes the local maxima of the modulus of the continuous Gaussian wavelet transform to produce candidate edge detection lines called maxima chains. These maxima chains from multiple wavelet scales are optimally sorted to produce a breast tissue segmentation mask. The mammographic mask is quantitatively compared to a manual delineation using the Dice-Sorenson Coefficient (DSC). The adaptation of the 2D WTMM segmentation method produces a median DSC of 0.9763 on 1042 mediolateral oblique (MLO) 2D Full Field Digital mammographic views from 82 patients obtained from the MaineHealth Biobank (Scarborough, Maine, USA). Our proposed approach is evaluated against OpenBreast, an open-source automated analysis software in MATLAB, through comparing each approach's masks to the manual delineations. OpenBreast produces a lower median DSC of 0.9710. To determine statistical significance, the analysis is restricted to 82 mammograms (one randomly chosen per patient), which yields DSC medians of 0.9756 for the WTMM approach vs. 0.9698 for OpenBreast (p-value = 0.0067 using a paired Wilcoxon Rank Sum test). Thus, the 2D WTMM segmentation method can reliably delineate the pectoral muscle and produce an accurate segmentation of whole breast tissue in mammograms.

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“…The classification methods regard the pectoral muscle segmentation as a dichotomous classification problem, that is, each pixel in the mammograms is classified into the target set or the non-target set. 8,19,[35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] In addition to the three types of methods, other methods are also proposed, such as discrete cosine transform. 51 Readers can be referred to Ganesan et al 6 for a detailed review on the methods of pectoral muscle segmentation.…”

Section: Introductionmentioning

confidence: 99%

Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

Wei¹,

Gwo²,

Huang³

2016

BJR

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Objective: X-ray mammography is a widely used and reliable method for detecting pre-symptomatic breast cancer. One of the difficulties in automatically computerized mammogram analysis is the presence of pectoral muscles in mediolateral oblique mammograms because the pectoral muscle does not belong to the scope of the breast. The objective of this study is to identify the boundary of obscure pectoral muscle in mediolateral oblique mammograms. Methods: Two tentative boundary curves are individually created to be the potential boundaries. To find the first tentative boundary, this study finds local extrema, prunes weak extrema and then determines an appropriate threshold for identifying the brighter tissue, whose edge is considered the first tentative boundary. The second tentative boundary is found by partitioning the breast into several regions, where each local threshold is tuned based on the local intensity. Subsequently, both of these tentative boundaries are used as the reference to create a refined boundary by Hough transform. Then, the refined boundary is partitioned into quadrilateral regions, in which the edge of this boundary is detected. Finally, these reliable edge points are collected to generate the genuine boundary by curve fitting. Results: The proposed method achieves the least mean square error 4.88 6 2.47 (mean 6 standard deviation) and the least misclassification error rate (MER) with 0.00466 6 0.00191 in terms of MER. Conclusion:The experimental results indicate that this method performs best and stably in boundary identification of the pectoral muscle. Advances in knowledge: The proposed method can identify the boundary from obscure pectoral muscle, which has not been solved by the previous studies.

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Automatic extraction of pectoral muscle in the MLO view of mammograms

Cited by 8 publications

References 20 publications

A novel multistage system for the detection and removal of pectoral muscles in mammograms

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Wavelet-Based Automatic Pectoral Muscle Segmentation for Mammograms

Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

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