A region-based active contour method for extraction of breast skin-line in mammograms

Thiruvenkadam, Sheshadri; Acharyya, Mausumi; Nv, Neeba; Jhunjhunwala, P.; Ranjan, Sohan

doi:10.1109/isbi.2010.5490383

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Active contours, or snakes, [19] have found many applications in image analysis and computer vision. This technique is used frequently to extract the breast skin-line in mammograms in [26], to help in multimodal treatment prostate cancer defined in [27] , to heart and trachea segmentation [28] and to the x-ray lung segmentation [29], to the leg ulcers segmentation [7]. The edge detection method developed in this paper is based on the active contour paradigm.…”

Section: Otsu's Methodsmentioning

confidence: 99%

Skin disease analysis and tracking based on image segmentation

Trabelsi¹,

Tlig²,

Sayadi³

et al. 2013

2013 International Conference on Electrical Engineering and Software Applications

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Tracking of the skin disease is a necessary step of diagnostic as well the measure of the wound's surface is very useful in healing's document. To overcome the difficulties of the skin illness's estimation, encountered with the currently used measurement techniques, we propose a novel approach aiming to reduce the time-consuming and the error rate. The proposed method is based on two steps; the first step is a preprocessing one which consists in image segmentation to detect the edge of the infected skin region. In the second one, another proposed method is applied to measure the wound 'size' and control the illness evolution. In this work, a comparative study was realized to select the most suitable segmentation technique referred to a proposed criterion based on 'edge accuracy' EAC. The new criterion was compared with the 'surface accuracy' based on ROC 1 space. The experiments show the performance of the proposed criterion and the efficacy of the measurement technique.

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Section: Otsu's Methodsmentioning

confidence: 99%

Skin disease analysis and tracking based on image segmentation

Trabelsi¹,

Tlig²,

Sayadi³

et al. 2013

2013 International Conference on Electrical Engineering and Software Applications

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show abstract

“…Differently from traditional models, Thiruvenkadam et al [ 27 ] divided the image into rectangular regions and defined two Gaussian models: one for the pixels of the object and another for the background pixels. Then, instead of searching the location of contour points of the active contour model, they tried to find optimal values for the Gaussian distribution of each rectangular region.…”

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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“…This consisted in using the Euclidean distance as constraint that was propagated to the upper and lower breast regions to obtain the complete skin-line estimate. Thiruvenkadan [12] obtained an initial contour using a fuzzy segmentation which was refined by a region-based active contour segmentation method. Wu [7] applied a dynamically adaptive thresholding to the gray level range in local regions of the breast periphery to obtain the initial contour.…”

Section: Introductionmentioning

confidence: 99%

Breast skin-line detection using dynamic programming

Lima

Correia

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, we present a novel method to extract the breast skin-line based on dynamic programming. Skin-line extraction is an important preprocessing step in CAD systems; however, it is a challenging problem due to the presence of noise, underexposed regions, which results in a low contrast area near the skin-air interface, and artifacts such as labels. Our proposal utilizes the stroma edge to constrain searching for the border. In order to cope with noise, we consider several candidate points for the border interface which are obtained by the Laplace operator applied in pre-defined directions in the mammogram. The breast contour is obtained from the candidate points using a dynamic programming algorithm. This utilizes a criterion of optimality to obtain the optimum contour by minimization of a cost function. The method was evaluated using 82 mammograms whose contour were manually extracted by a radiologist from the mini-MIAS database. The Polyline Distance Measure was evaluated for each contour selected with the proposed method, obtaining a mean error of 2.05 pixels and a standard deviation of 0.80.

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A region-based active contour method for extraction of breast skin-line in mammograms

Cited by 9 publications

References 14 publications

Skin disease analysis and tracking based on image segmentation

Skin disease analysis and tracking based on image segmentation

A Probabilistic Approach for Breast Boundary Extraction in Mammograms

Breast skin-line detection using dynamic programming

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