Extracting and smoothing contours in mammograms using Fourier descriptors

Abstract

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.

Two‐step evidential fusion approach for accurate breast region segmentation in mammograms

“…Thresholds are automatically selected based on the zeroth and the first-order cumulative moments of the grey-level histogram. In [16], Feudjio et al introduced a local adaptive segmentation and a smoothing method based on Fourier descriptors to extract concurrent contours in mammograms. This method was tested on a set of mammograms and has shown good results according to the authors.…”

Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors

“…1D Fourier Descriptors (FDs): The shape signature is widely used as a discriminative element in the cell recognition. Several studies have reported that shape descriptor based contour is one of the computation methods which has many advantages in recognizing geometric signatures, as it is easy to implement and promising a good matching performance [31], [32]. Therefore, 1D FDs is introduced as only DCs have the irregular shapes [33].…”

“…In the case of an object contour, low frequency components of FDs contain information about the shape of the contour. In the meantime, the finer details along the contour are included in the higher frequency components [31]. At the first stage, the image is converted into grayscale and normalized.…”

Automatic extraction of pectoral muscle in the MLO view of mammograms

“…-Breast contour detection: we use the approach described in Feudjio et al (2012). Note that the main pattern for breast orientation detection is the breast shape.…”

“…In most CAD systems, a prior extraction of patterns like breast contour (Feudjio et al 2012, Karnan and Thangavel 2007, Liu et al 2011, nipple (Karnan and Thangavel 2007) and pectoral muscle (Ferrari et al 2004, Kwok et al 2004, Liu et al 2011 have to be carried out to allow accurate analysis of breast tissues. In this paper, we focus on the pectoral muscle extraction step.…”