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. 2013 Feb;19(1):e20-6.
doi: 10.1111/j.1600-0846.2011.00602.x. Epub 2012 Jan 11.

Automatic dirt trail analysis in dermoscopy images

Beibei Cheng  1 , R Joe Stanley, William V Stoecker, Christopher T P Osterwise, Sherea M Stricklin, Kristen A Hinton, Randy H Moss, Margaret Oliviero, Harold S Rabinovitz
Affiliations

Automatic dirt trail analysis in dermoscopy images

Beibei Cheng et al. Skin Res Technol. 2013 Feb.

Abstract

Background: Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails.

Methods: In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions.

Results: For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach.

Conclusion: Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation.

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Figures

Figure 1
Figure 1
Dirt trail examples in dermoscopic skin lesion images, shown by arrows, with dirt trails containing dots and clods of varying sizes.
Figure 2
Figure 2
Overview of the dirt trail detection algorithm.
Figure 3
Figure 3
RGB plane. (a) Red plane. (b) Green plane. (c) Blue plane.
Figure 4
Figure 4
Gaussian bandpass filter representation in the spatial frequency domain. The middle frequencies are kept.
Figure 5
Figure 5
Bandpass-filtered images converted to spatial domain for R, G, and B planes. (a) Red plane. (b) Green plane. (c) Blue plane. The original color plane images are on the left, and the filtered images WR, WG, and WB are on the right.
Figure 6
Figure 6
Median filter output images from R,G,B planes. (a) FR, (b) FG, (c) FB.
Figure 7
Figure 7
Output images from scalarized Otsu method from R,G,B planes. (a) TR, (b) TG, (c) TB.
Figure 8
Figure 8
Otsu output image A after logical ANDing of the threshold color plane images.
Figure 9
Figure 9
Image overlay. (a) Image overlay R, after hair and bubble removal. (b) Dirt trail image overlay K after isolated object removal.
Figure 10
Figure 10
Dirt trail detection mask examples. (a) Dirt trail image. (b) Dirt trail image overlay. (c) Benign image. (d) Benign image overlay.
Figure 11
Figure 11
ROC curve and AUC (area under curve) for backpropagation neural network. AUC=0.902.
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