A Radiomic feature-based Nipple Detection Algorithm on Digital Mammography

In the diagnosis and detection of breast lesions, the nipple is an important anatomical landmark which can be used for the registration on multiview mammograms. In this study, we propose a new detection algorithm for nipples on digital mammography (DM) by applying pixel classification based on geometric and radiomic features extracted from breast boundary regions.

The imaging characteristics of nipples are closely related to the visibility on mammograms. To locate the nipple on mammogram, a searching area is first determined based on the breast boundary and chest wall orientation. Two different approaches are developed for obvious and subtle nipples, respectively. For obvious nipples, top hat transformation is employed to detect the nipple region, whose geometric center is regarded as the nipple position. For subtle nipples, the curved searching area near the breast boundary is mapped onto a Cartesian plane through a revised rubber band straightening transformation. On the straightened searching area, the geometric and radiomic features are calculated along the normal direction of the breast boundary, and a random forest classifier is trained for subtle nipple localization.

Seven hundred and twenty-one DMs were collected for the evaluation of the proposed algorithm. The locations of nipples are manually identified by an experienced radiologist as the reference standard. The average Euclidean distance between the computed nipple position and the reference standard was 2.69 mm (obvious) and 7.81 mm (subtle), respectively. A total of 97.61% of the obvious nipples (613/628) and 88.17% of the subtle nipples (82/93) were detected within a 10-mm radius centered from the reference standard.

The evaluation results show that the proposed method is effective for nipple detection on DM, especially for subtle nipple detection.