Automatic Nipple Detection Method for Digital Skin Images with

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An automated nipple detection method that performs very well even in the presence of heavy hair, severe and mild lesions, and various nipple sizes, with an overall nipple detection accuracy of 95.14% across the evaluation set is presented. The presence of nipples in human trunk images is considered a main problem in psoriasis images. Existing segmentation methods fail to differentiate between psoriasis lesions and nipples due to the high degree of visual similarity. In this paper, we present an automated nipple detection method as an important component for severity assessment of psoriasis. First, edges are extracted using Canny edge detector where the smoothing sigma parameter is automatically customized for every image based on psoriasis severity level. Then, circular hough transform (CHT) and local maximum filtering are applied for circle detection. This is followed by a nipple selection method where we use two new nipple similarity measures, namely: hough transform peak intensity value and structure similarity index. Finally, nipple selection refinement is performed by using the location criteria for the selected nipples. The proposed method is evaluated on 72 trunk images with psoriasis lesions. The conducted experiments demonstrate that the proposed method performs very well even in the presence of heavy hair, severe and mild lesions, and various nipple sizes, with an overall nipple detection accuracy of 95.14% across the evaluation set.