Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection

Kwok, S.M.; Chandrasekhar, Ramachandran; Attikiouzel, Y.

doi:10.1109/anziis.2001.974051

Cited by 46 publications

(25 citation statements)

References 3 publications

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“…The hypotenuse of the triangle pectoral muscle shows a curved structure rather than an exact line. Studies that reveal exact lines using dynamic programming also exist in the literature [16][17][18]22]. A survey of studies utilizing statistical methods exhibits that graph-based approaches have recently been used for pectoral muscle removal [24][25][26].…”

Section: Related Workmentioning

confidence: 99%

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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%

“…Line-detection methods aim to determine the hypotenuse of this triangle. For this reason, straight line estimation [4,11,[16][17][18][19][20][21], Hough transform [14,22], and curve fitting [23] are used. The hypotenuse of the triangle pectoral muscle shows a curved structure rather than an exact line.…”

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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“…Previous work related to pectoral muscle suppression used Hough Transform [14,29], assuming that the boundary between the pectoral muscle and the breast can be approximated by a straight line. Other related works are the one of Yam et al [30] whose work introduces a curvature component to the Hough estimation and the work presented by Ferrari et al [31] who propose a polynomial modelling of the pectoral muscle.…”

Section: Our Methodsmentioning

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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“…The final segmentation results have been thoroughly assessed by two mammographic experts. An early version of this work was described in [3]. …”

Section: A Literature Reviewmentioning

confidence: 99%

“…We contend that before the digitized mammogram is analyzed by computer, it must be segmented into its representative anatomical regions. Three anatomical landmarks have to be first extracted automatically: they are the breast border [1], the nipple [2] and the pectoral muscle [3]. In this paper, we propose a method for automatically segmenting the pectoral muscle on mediolateral oblique (MLO) view mammograms.…”

mentioning

confidence: 99%

Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms

Kwok¹,

Chandrasekhar²,

Attikiouzel³

et al.

Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer

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Abstract-Mammograms are X-ray images of the breast which are used to detect breast cancer. When mammograms are analyzed by computer, the pectoral muscle should preferably be excluded from processing intended for the breast tissue. For this and other reasons, it is important to identify and segment out the pectoral muscle. In this paper, a new, adaptive algorithm is proposed to automatically extract the pectoral muscle on digitized mammograms; it uses knowledge about the position and shape of the pectoral muscle on mediolateral oblique views. The pectoral edge is first estimated by a straight line which is validated for correctness of location and orientation. This estimate is then refined using iterative "cliff detection" to delineate the pectoral margin more accurately. Finally, an enclosed region, representing the pectoral muscle, is generated as a segmentation mask. The algorithm was found to be robust to the large variations in appearance of pectoral edges, to dense overlapping glandular tissue, and to artifacts like sticky tape. The algorithm has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 images. The segmentation results were evaluated by two expert mammographic radiologists, who rated 83.9% of the curve segmentations to be adequate or better.

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Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection

Cited by 46 publications

References 3 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

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms

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