Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images

Abstract

Background: Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing these vessels from other telangiectasia, that are commonly found in sun-damaged skin, is challenging.

Methods: Image analysis techniques are investigated to find vessels structures in BCC automatically. The primary screen for vessels uses an optimized local color drop technique. A noise filter is developed to eliminate false-positive structures, primarily bubbles, hair, and blotch and ulcer edges. From the telangiectasia mask containing candidate vessel-like structures, shape, size and normalized count features are computed to facilitate the discrimination of benign skin lesions from BCCs with telangiectasia.

Results: Experimental results yielded a diagnostic accuracy as high as 96.7% using a neural network classifier for a data set of 59 BCCs and 152 benign lesions for skin lesion discrimination based on features computed from the telangiectasia masks.

Conclusion: In current clinical practice, it is possible to find smaller BCCs by dermoscopy than by clinical inspection. Although almost all of these small BCCs have telangiectasia, they can be short and thin. Normalization of lengths and areas helps to detect these smaller BCCs.

Keywords: basal cell carcinoma; dermoscopy; image analysis; neural network; telangiectasia; vessels.

Fig. 1

Telangiectasia, arborizing telangiectasia (trunk and branches) are the classical telangiectasia seen in a contact, non-polarized dermsocopy image of basal cell carcinoma (BCC). Fine telangiectasia are more numerous than arborizing telangiectasia in BCC. Note that sun damage telangiectasia are often rudimentary and tend to be wider, shorter, have less sharp edges, have a greater variation in width, and are less numerous per area than BCC telangiectasia.

Fig. 2

Overview – basal cell carcinoma (BCC) diagnosis by telangiectasia detection.

Fig. 3

Direction mask used for pixel marking.

Fig. 4

Mask image with different red drops. (a) Original image. (b) Mask image with red drop of 2. (c) Mask image with red drop of −2.

Fig. 5

Mask images with different NumPix values. (a) Original Image, (b) NumPix = 4, (c) NumPix = 7.

Fig. 6

Brown area filtering. (a) Brown areas labeled as vessels. (b) Brown areas after removal from vessel mask by G>B+5 Filter. (c) Brown areas after removal from vessel mask by G>B+20 filter. Filter shown in (b) is optimal.

Fig. 7

Hair filtering. (a) Hair labeled as vessels. (b) Hair areas after removal by R/G>0.01 Filter. (c) Hair areas after removal R/G>0.02 filter. Filter shown in (b) is optimal.

Fig. 8

Bubble filtering. (a) Original image, (b) Bubble labeled as vessels, (c) Unmarked bubble using the bubble detection algorithm presented above.

Fig. 9

Blob density filtering. (a)Original image. (b)Big blob labeled as vessel. (c)Unmarked big blob. (d) Big blob unmarked with 80% density.

Fig. 10

(a) Mask after noise filtering. (b) Mask after dilation, radius 3, and erosion, radius 2.

Fig. 11

Lower area bound (a) Mask with noise. (b) Mask after area lower bound.

Fig. 12

Receiver operating characteristic curve and area under curve (AUC) results for different feature combinations. (a) All 30 features from Table 1 with AUC = 0.9548. (b) General descriptors and object area descriptors with AUC = 0.9670. (c) General descriptors and the object number descriptors with AUC = 0.9482. (d) Reduced feature set selected using SAS Procedure logistic with AUC = 0.9547.

Fig. 13

Basal cell carcinoma misdiagnosed. (a) Original image, (b) Telangiectasia mask.

Fig. 14

Benign lesion misdiagnosed. (a) Original image, (b) Telangiectasia mask.