Directional SUSAN image boundary detection of breast thermogram

Abstract

Thermography of the breast has been shown to be well suited to detect early signs of breast cancer. This study proposes a novel method for boundary detection of breast thermography images using directional SUSAN method. Among breast thermography image processing steps, breast isolation from background and from each other is an essential stage for proper detection of breast cancer. For this purpose, in this study, breast boundary is grouped into three regions depending on the region property. The algorithm of boundary detection is different for each region. Specially, for bottom breast boundary, directional SUSAN edge detector is presented that uses two rectangle masks to create a directional SUSAN gradient image with emphasis on oblique useful edges and omitting undesirable ones. Then cubic parabolic interpolation is implemented to determine a set of edge points on the boundaries. At last, an effective search algorithm is executed to correct some false points in order to extract breast boundaries accurately. The performance of the proposed approach illustrated by applying on the images of three databases. Experimental results show that this method acts effectively and confirm the accurate boundary detection. Moreover, statistical measures are calculated to indicate the remarkable capabilities of the proposed approach.

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

“…A study in [61] proposed a novel method to detect the boundary of breast thermograms using a Single Univalue Segment Assimilating Nucleus (SUSAN); an edge-based segmentation technique. e Hough transform was used to determine the center points of each breast after the armpit region by hand.…”

The Effect of Thermography on Breast Cancer Detection-A Survey

“…Satisfactory results are obtained but with certain limitations with generalization. Elham Mahmoudzadeh et al 18 proposed a novel method for boundary detection. Here breast boundary is grouped into three regions depending on region property.…”

A New Objective Evaluation Method on Image Edge Detection

“…So far, there are many new models and algorithms for image denoising and edge detection [7] [8] . However, there is no quantitative numerical standard for the evaluation of edge detection results, which is often judged by the subjective evaluation method of the naked eyes [9][10] [11] .Due to the subjectivity of subjective evaluation methods, even for the same test results, the evaluation results are often different in different environments and different atmospheres [12][13] [14] .Therefore, a specific quantitative numerical index to evaluate the quality of the detection results is needed to be establish. Generally speaking, for mainstream computer vision, the results of edge detection should meet the following requirements, namely, canny proposed three criteria of edge detection in 1986 [15][16] [17] [18] .…”