A fuzzy approach to segmenting the breast region in mammograms

Wirth, Michael A.; Lyon, Jennifer; Nikitenko, D.

doi:10.1109/nafips.2004.1336329

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…Rickard et al [24] presents Self-organizing map, a type of unsupervised artificial neural network model. The method applied by Wirth et al [25] was a fuzzy segmentation and evaluates the results in terms of completeness and correctness comparing the images from the MIAS database with a gold standard manually generated. Recently, Tromans et al [26], use a mixture model to obtain a mathematical representation of the image background and the compressed parameters, combined with a Fourier model, using an Expectation Maximization algorithm.…”

Section: -Polynomial Modelling Based Techniques An Early Methods Propmentioning

confidence: 99%

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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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Section: -Polynomial Modelling Based Techniques An Early Methods Propmentioning

confidence: 99%

“…Table 1 shows the tendencies and distribution of methods from the firsts works to recent approaches. [24] Wirth04 [25] Tromans04 [26] -Histogram based techniques. Probably one of the first attempts to separate the breast region was presented by Hoyer et al [2] and it was done using simple histogram thresholding.…”

Section: Work On Breast Region Segmentationmentioning

confidence: 99%

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

Raba

Oliver

Martı́

et al. 2005

Pattern Recognition and Image Analysis

125

View full text Add to dashboard Cite

show abstract

“…[24] presents Self-organizing map, a type of unsupervised artificial neural network model. The method applied by [25] was a fuzzy segmentation and evaluates the results in terms of completeness and correctness comparing the images from the MIAS database with a gold standard manually generated. Recently, [26], use a mixture model to obtain a mathematical representation of the image back-ground and the compressed parameters, combined with a Fourier model, using an Expectation Maximization algorithm.…”

Section: Work On Breast Region Segmentationmentioning

confidence: 99%

A Method For Improve Preprocessing Images Mammography

Dehghani¹,

Dezfooli²

2011

IJIET

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Abstract-A great volume of mammography images have dark background, these parts are not important in processing of mammography images. We can decrease the picture size and increase the processing speed by deleting these parts. Some parts of images have some notes and labels which consist of information like name, family, hospital's seal, date and…. It is better to omit these labels before processing since they are bright; the same as gray level of some breast tissues and have the same gray level. In the other way it is better to limit processing to the breast region and omit excessive parts. Previous studies over breast tissues show that if the considered region has not been marked clearly, it will influence characteristics and will not give the desired result. For obtaining this aim first find the main breast region, and then omit excessive parts to obtain processing result accurately and rapidly. The work that we do in this paper is introduced in three phases. The first phase is omitting the excessive image parts which are in the two sides of the image; we do this work by the usage of the pixels brightness. The second phase is the distinction of the breast direction and put all images in one direction; we do this work by the usage of threshold limit of gray level of the two halves of the image. The third phase is the breast region segmentation from the background; we do this work by the usage of series of point operations and the growing region method and the result has been reported to 99%.

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“…Lots of segmentation methods for breast region have been reported [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], including histogram thresholding [1,2], simple thresholding and morphological filtering [3][4][5][6], gradient based segmentation techniques [7][8][9][10][11], polynomial modeling based methods [13][14][15], active contour methods [17][18][19][20][21], and classification based techniques [22][23][24][25]. Existing breast region segmentation methods upon mammograms have been reviewed in detail elsewhere [16].…”

Section: Introductionmentioning

confidence: 99%

Robust segmentation using kernel and spatial based fuzzy c-means methods on breast x-ray images

2008

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Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images. However, this task is challenging because the acquired images not only are inherent noisy and inhomogeneous, but there are also connected or overlapped artifacts, or noises on the images as well, due to local volume effect of tissues, parametric resolutions and other physical limitations of the imaging device. This paper proposes and develops robust fuzzy c-means (FCM) segmentation methods for segmentation of breast region on breast x-ray images, including mammography and tomosynthesis, respectively. We develop spatial information-and kernel function-based FCM methods to differentiate breast area or breast volume. Spatial information based FCM method incorporates neighborhood pixels' intensities into segmentation because neighbored pixels on an image are highly correlated. Kernel based FCM algorithm is developed by transforming pixel intensity using kernel functions to better improve segmentation performance. The proposed segmentation methods are implemented on mammograms and tomosynthetic images and compared with conventional FCM results. Experiment results demonstrate the proposed segmentation methods are much better compared with traditional FCM method, and are more robust to noises. The developed kernel and spatial based FCM method will be applied for differentiation of breast density and abnormal regions within the breast region to examine its performance in reducing false positive segmentations.

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A fuzzy approach to segmenting the breast region in mammograms

Cited by 18 publications

References 12 publications

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

A Method For Improve Preprocessing Images Mammography

Robust segmentation using kernel and spatial based fuzzy c-means methods on breast x-ray images

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