An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms

Saltanat, Najmus; Hossain, Mohammad Amzad; Alam, Mohammed Shafiq

doi:10.1109/bicta.2010.5645272

Cited by 19 publications

(8 citation statements)

References 12 publications

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“…The Pectoral Margin is not, as others presume, a straight line [21], but it has a largely concave margin on the upper side of the nipple, concave or convective, or a mixture of the two for the lower part of the Margin. The variation of the margin between mammogram is so complicated that there is no single geometric or mathematical model available [14]. A lot of techniques have been suggested to fix this issue in the last few years.…”

Section: Pectoral Muscle Presence In the Mammography And Their Impactmentioning

confidence: 99%

“…The distinct kinds of noise can be such as high intensity label, low intensity label, scanning artefact, tape artefact, light leakage, low breast skin contrast and imperfections in the scanning method. [14]. Removing noises from the images required certain steps.…”

Section: E Significancementioning

confidence: 99%

“…A statistical region growing algorithm is implemented and the pectoral muscle is lastly suppressed and the border is refined with a morphological operator. N. Saltanat et al [53] suggested a technique capable of segmenting pectoral muscles in a wide spectrum of sizes, shapes and positions. This algorithm is robust not only for big differences in pectoral muscle size, shape and position, but also for any kind of artifacts such as medical tags.On 322 pictures of the Mammographic Image Analysis Society (MIAS) database, the algorithm was implemented.…”

Section: A Intensity Based Approachmentioning

confidence: 99%

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Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Saxena¹,

Singh²,

Agarwal³

2020

IJEAT

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Breast cancer is class of cancer that sets off when the breast cells grow out of proportion and control. The radiologist recognizes the sign of breast cancer by performing a kind of X-ray called screening mammography. During analysis of mammography the biggest problem arise because of the presence of pectoral muscle. The mass of tissue on which the breast is rest called the Pectoral muscle. The primary problem is that pectoral muscle area density is almost similar to the tumour cell and this condition generates confusion to recognize the tumour cell. For analysis the Medio-Lateral oblique (MLO) views of mammograms is being taken so that the complete breast image can be viewed. Some part of the pectoral muscle also gets visible along with the breast in the MLO view which must be segmented from the mammogram. Pectoral muscle involvement can lead to false positive or false negatives. The workforce shortages of Radiologist with respect to growing demands and to declare the result in a very short time have also increased the pressure. Consequently, a radiologist is sometimes unable to detect an anomaly. This is the time where CAD system can help radiologists to detect breast cancer at an earlier stage. Numerous strategies for the selection of the pectoral muscle have been suggested and developed so far. This article reviews the different segmentation techniques for pectoral muscle removal in mammograms through digital images..

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Section: Pectoral Muscle Presence In the Mammography And Their Impactmentioning

confidence: 99%

Section: E Significancementioning

confidence: 99%

Section: A Intensity Based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Saxena¹,

Singh²,

Agarwal³

2020

IJEAT

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“…Intensity-based approaches are based on the fact that the intensity range of a pectoral muscle region should be higher than the range of breast parenchyma. These approaches directly utilize the pixel intensities [2][3][4][5][6][7], image histograms [8][9][10], and image gradients [11], or they are applied to image gradients [12]. Additionally, there are also some studies that segment pectoral muscles in wavelet domain instead of spatial domain [13][14][15].…”

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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“…6 Intensitybased methods identify breast tissues and the pectoral muscle based on intensity differences along the potential boundary region. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] The success of this kind of method depends upon the choice of an appropriate threshold. Inconsistent of intensity change may affect the segmentation results.…”

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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An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms

Cited by 19 publications

References 12 publications

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

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

Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

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