Ms. Mei Ge Profile

Mei Ge

at Sunnybrook Research Institute

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SPIE Journal Paper | 25 November 2014

Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximum a posteriori model

Mei Ge, James Mainprize, Gordon Mawdsley, Martin Yaffe

JMI, Vol. 1, Issue 03, 034503, (November 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.3.034503

KEYWORDS: Image segmentation, Mammography, Tissues, Image processing, Breast, Image processing algorithms and systems, Magnetorheological finishing, Chest, Expectation maximization algorithms, Algorithm development

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Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semielliptic-shaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either “For Processing” or “For Presentation” image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM “For Processing” images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types.

Proceedings Article | 9 March 2011 Paper

Automatic identification of pectoralis muscle on digital cranio-caudal-view mammograms

Mei Ge, Gordon Mawdsley, Martin Yaffe

Proceedings Volume 7963, 79631W (2011) https://doi.org/10.1117/12.878785

KEYWORDS: Image segmentation, Breast, Mammography, Tissues, Chest, Roentgenium, Transform theory, Binary data, Image processing, Signal attenuation

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To improve efficiency and reduce human error in the computerized calculation of volumetric breast density, we have developed an automatic identification process which suppresses the projected region of the pectoralis muscle on digital CC-view mammograms. The pixels in the image of the pectoralis muscle, represent dense tissue, but not related to risk, will cause an error in estimated breast density if counted as fibroglandular tissue. The pectoralis muscle on the CC-view is not always visible and has variable shape and location. Our algorithm robustly detects the existence of the pectoralis in the image and segments it as a semi-elliptical region that closely matches manually segmented images. We present a pipeline where adaptive thresholding and distance transforms have been used in the initial pectoralis region identification process; statistical region growing is applied to explore the region within the identified location aimed at refining the boundary; and a 2D shape descriptor is developed for the target validation: the segmented region is identified as the pectoralis muscle if it has a semi-elliptical contour. After the pectoralis muscle is identified, a 1D-FFT filtering is used for boundary smoothing. Quantitative evaluation was performed by comparing manual segmentation by a trained operator, and analysis using the algorithm in a set of 174 randomly selected digital mammograms. Use of the algorithm is shown to improve accuracy in the automatic determination of the volumetric ratio of breast composition by removal of the pectoralis muscle from both the numerator and denominator. As well, it greatly improves the efficiency and throughput in large scale volumetric mammographic density studies where previously interaction with an operator was required to obtain that level of accuracy.

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