Automatic Detection of Blue-White Veil by Discrete Colour Matching in Dermoscopy Images

Abstract

Abstract. Skin lesions are often comprised of various colours. The presence of multiple colours with an irregular distribution can signal malignancy. Among common colours under dermoscopy, blue-grey (blue-white veil) is a strong indicator of malignant melanoma. Since it is not always easy to visually identify and recognize this feature, a computerised automatic colour analysis method can provide the clinician with an objective second opinion. In this paper, we put forward an innovative method, through colour analysis and computer vision techniques, to automatically detect and segment blue-white veil areas in dermoscopy images. The proposed method is an attempt to mimic the human perception of lesion colours, and improves and outperforms the state-of-the-art as shown in our experiments.

Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision

“…whether the image contains the feature or not). For comparison, we considered the most prominent studies amongst the prior arts: Celebi et al [8] and Madooei et al [9]. These studies report a method, experimental procedure, and results specifically pertaining to the detection of the feature under study here.…”

“…Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from employing low-level colour features, such as simple statistical measures of colours occurring in the lesion [6], [7], to availing themselves of high-level semantic features such as presence of blue-white veil [8], [9] or colour variegation [10], [11] in the lesion.…”

“…This indicates that their method can benefit from the choice of colour representation. To investigate this, Madooei et al [9] reproduced Celebi's training experiment where each pixel is represented by its corresponding coordinates in various colour spaces. Their investigation revealed that by thresholding over the Luminance channel (Lum = R + G + B) and normalized blue (nB = B/Lum), one can obtain equally good results with considerably less computation compared to [8].…”

“…Madooei et al [9] identified the blue-white veil feature in each dermoscopy image through a nearest neighbour matching of image regions to colour patches of a "blue-white veil colour palette": a discrete set of Munsell colours best describing the feature. The palette was created by mapping instances of veil and non-veil data to Munsell colour patches, keeping these colours that exclusively described the feature with highest frequency.…”

“…Celebi et al [15] 2006 Decision tree Pixel-based Celebi et al [8] 2008 Decision tree Pixel-based Ogorzalek et al [13] 2010 Thresholding Pixel-based Sforza et al [14] 2011 Thresholding Pixel-based Devita et al [16] 2012 LMT Region-based Wadhawan et al [18] 2012 SVM Region-based Madooei et al [9] 2013 Thresholding Pixel-based Madooei et al [34] 2013 Colour Palette Region-based Fabbrocini et al [17] 2014 LMT Region-based Lingala et al [19] 2014 Fuzzy sets Pixel-based In all the studies reported here, the emphasis is to use colour features, and structural information such as texture are either ignored [13], [16]- [19] or found to be not useful [8], [9]. This is problematic since these detectors would potentially fail to distinguish between a BWS and a similar feature in a benign lesion.…”