A fully automated complete segmentation scheme for mammograms

Tzikopoulos, Stylianos; Georgiou, Harris V.; Mavroforakis, Michael E.; Dimitropoulos, Nikos

doi:10.1109/icdsp.2009.5201262

Cited by 9 publications

(3 citation statements)

References 10 publications

(13 reference statements)

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“…This random variability in pectoral muscle position coupled with its resemblance to breast tissues in density makes segmentation a difficult task. As a result, many of the techniques discussed in the literature are not fully automatic [27] [28] [29] [79]. 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.…”

Section: Pectoral Muscle Presence In the Mammography And Their Impactmentioning

confidence: 99%

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%

Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images

Saxena¹,

Singh²,

Agarwal³

2020

IJEAT

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“…To this end, Tzikopoulos et al [9, 10], first, aligned the image using the chest wall location. In order to determine the chest wall location, they used the decreasing pixel intensity of the breast tissue.…”

Section: Previous Workmentioning

confidence: 99%

“…Since a mammogram usually contains noise and other artefacts such as labels, it is reasonable to add preliminary steps to remove them from the image. To this end, Tzikopoulos et al [ 9 , 10 ], first, aligned the image using the chest wall location. In order to determine the chest wall location, they used the decreasing pixel intensity of the breast tissue.…”

Section: Previous Workmentioning

confidence: 99%

A Probabilistic Approach for Breast Boundary Extraction in Mammograms

Aghdam

Puig

Solanas

2013

Computational and Mathematical Methods in Medicine

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The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

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