access icon free Directional SUSAN image boundary detection of breast thermogram

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.

Inspec keywords: statistical analysis; interpolation; cancer; edge detection; medical image processing; infrared imaging

Other keywords: early sign detection; effective search algorithm; directional SUSAN gradient image; directional SUSAN image boundary detection method; breast thermography image processing steps; cubic parabolic interpolation; statistical measures; breast cancer

Subjects: Other topics in statistics; Numerical approximation and analysis; Other topics in statistics; Patient diagnostic methods and instrumentation; Interpolation and function approximation (numerical analysis); Biomedical measurement and imaging; Image recognition; Biology and medical computing; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Probability theory, stochastic processes, and statistics

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2015.0347
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