Accurate Breast Region Detection in Digital Mammograms Using a Local Adaptive Thresholding Method

Shahedi, B.K.M.; Amirfattahi, Rassoul; Azar, Farah Torkamani; Sadri, Saeed

doi:10.1109/wiamis.2007.15

Cited by 17 publications

(11 citation statements)

References 3 publications

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“…We have also compared our work with Fast-Marching Method Yapa, Koichi Harada [35] and Local Adaptive Thresholding Method of Maysam Shahedi B K, Rassoul Amirfattahi, Farah Torkamani Azar and Saeed Sadri [34]. These two research papers are also very systematic and algorithm's performance evaluation part is well organized.…”

Section: Resultsmentioning

confidence: 99%

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Maitra¹,

Nag²,

Bandyopadhyay³

2011

IJCA

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Computer Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images.In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini-MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

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Section: Resultsmentioning

confidence: 99%

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Maitra¹,

Nag²,

Bandyopadhyay³

2011

IJCA

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“…Clustering segmentation methods have been employed in [50,42,59,45,11,61,60,14,64]. Regions of interest segmentation using a single view and the multiple views are used to segment both masses and calcifications, those methods have been used in the work of [49,29,23,51,37,58,66,10,40,53,39,9,15,18].…”

Section: Related Workmentioning

confidence: 99%

Mammogram image visual enhancement, mass segmentation and classification

Al-Najdawi

Biltawi

Tedmori

2015

Applied Soft Computing

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“…However, as a single threshold is used for partitioning the image, it will inevitably result in misclassification of some background pixels as the breast region and vice-versa due to overlap between these regions. The use of local thresholding [9,14] had been shown to give better results as it thresholds each pixel using local information from its neighborhood. This method can efficiently deal with noise present on the breast edge area.…”

Section: Related Workmentioning

confidence: 99%

“…Active contour methods are initialization dependent and sometimes a prior segmentation must be done to approximate the shape of the object in order to speed up the convergence. This technique is also complex and time consuming and can impose limitation on practical applications [9,10,30].…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, in mammograms, breast edge is not clearly visible and the image background sometimes contains artifacts or is corrupted by noise. Developing a contour detection algorithm able to deal with different kinds of noise and artifacts present in the background of a mammogram is hard to achieve since it is difficult to distinguish the skin-air interface that separates the breast tissue from the background [9,10]. As a result, pixels are often shifted or misestimated after segmentation from the true contour.…”

Section: Introductionmentioning

confidence: 99%

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Extracting and smoothing contours in mammograms using Fourier descriptors

Feudjio¹,

Tiedeu²,

Noubeg³

et al. 2014

JBiSE

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Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object's contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.

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Accurate Breast Region Detection in Digital Mammograms Using a Local Adaptive Thresholding Method

Cited by 17 publications

References 3 publications

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Mammogram image visual enhancement, mass segmentation and classification

Extracting and smoothing contours in mammograms using Fourier descriptors

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