Integrating multiscale polar active contours and region growing for microcalcifications segmentation in mammography

Abstract

Morphology of individual microcalcifications is an important clinical factor in microcalcification clusters diagnosis. Accurate segmentation remains a difficult task due to microcalcifications small size, low contrast, fuzzy nature and low distinguishability from surrounding tissue. A novel application of active rays (polar transformed active contours) on B-spline wavelet representation is employed, to provide initial estimates of microcalcification boundary. Then, a region growing method is used with pixel aggregation constrained by the microcalcification boundary estimates, to obtain the final microcalcification boundary. The method was tested on dataset of 49 microcalcification clusters (30 benign, 19 malignant), originating from the DDSM database. An observer study was conducted to evaluate segmentation accuracy of the proposed method, on a 5-point rating scale (from 5:excellent to 1:very poor). The average accuracy rating was 3.98±0.81 when multiscale active rays were combined to region growing and 2.93±0.92 when combined to linear polynomial fitting, while the difference in rating of segmentation accuracy was statistically significant (p <0.05).

Mammography - Recent Advances

“…A number of authors have utilized a wide range of quantitative individual MC morphology properties (Patrick et al, 1991;Shen et al, 1994;Papadopoulos et al, 2008;Sklansky et al, 2000;Jiang et al, 1996 A disadvantage of these methods is that their performance is dependent on the accuracy of the segmentation method used. Specifically, the segmentation accuracy of less robust segmentation methods reduces the performance of morphology-based CADx schemes (Paquerault et al, 2004;Arikidis et al, 2009).…”

Computerized Image Analysis of Mammographic Microcalcifications: Diagnosis and Prognosis

“…A disadvantage of these methods is that their performance is dependent on the accuracy of the segmentation method used. Specifically, the segmentation accuracy of less robust segmentation methods reduces the performance of morphology-based CADx schemes (Paquerault et al, 2004;Arikidis et al, 2009). …”