Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

Chakraborty, Jayasree; Mukhopadhyay, Sudipta; Singla, Veenu; Khandelwal, Niranjan; Bhattacharyya, Pinakpani

doi:10.1007/s10278-011-9421-y

Cited by 51 publications

(44 citation statements)

References 11 publications

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“…Another common performance measure pectoral muscle segmentation is the average rate of false positives (FP) and false negatives (FN) found in a segmentation. Both of these coefficients, as well as the average Hausdorff distance, for our method and the techniques suggested in [6], [7], [4], [8], and [5] are displayed in Table 3. We see that our method slightly outperforms the other techniques.…”

Section: Resultsmentioning

confidence: 99%

“…For example Kwok et al [2] suggested Hough transform followed by cliff detection to progressively refine the obtained line into a curve that better fits the pectoral muscle boundary. In [4], the authors also detect the pectoral muscle initially as a straight line and then they look for local gradient maxima within a band enclosing it. Likewise, Kinoshita et al [5] approximated the pectoral muscle by the longest straight line in the Radon domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

Galdrán

Picón²,

Garrote³

et al. 2015

Pattern Recognition and Image Analysis

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Abstract. Pectoral muscle segmentation on medio-lateral oblique views of mammograms represents an important preprocessing step in many mammographic image analysis tasks. Although its location can be perceptually obvious for a human observer, the variability in shape, size, and intensities of the pectoral muscle boundary turns its automatic segmentation into a challenging problem. In this work we propose to decompose the input mammogram into its textural and structural components at different scales prior to dynamically thresholding it into several levels. The resulting segmentations are refined with an active contour model and merged together by means of a simple voting scheme to remove possible outliers. Our method performs well compared to several other state-ofthe-art techniques. An average DICE similarity coefficient of 0.91 and mean Hausdorff distance of 3.66 ± 3.23 mm. validate our approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

Galdrán

Picón²,

Garrote³

et al. 2015

Pattern Recognition and Image Analysis

View full text Add to dashboard Cite

show abstract

“…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%

“…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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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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“…Under an assumption that the muscle boundary is a line/curve, the line/curve detection methods propose various methods to identify or simulate the line/curve. [27][28][29][30][31][32][33][34] The main difficulty in this kind of methods is that if the pectoral muscle boundary is obscure, a line approximation or curve fitting is also difficult to perform. 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.…”

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 Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

Cited by 51 publications

References 11 publications

Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

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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