Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes

Abstract

Background: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin.

Methods: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated.

Results: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist.

Conclusions: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.

Fig. 1

Dermoscopy image of a dysplastic compound nevus, a benign pigmented skin lesion.

Fig. 2

Negative of blurred gray-level image used for snake initialization.

Fig. 3

Histogram of negative of blurred image used for thresholding. The Otsu and relaxed thresholds obtained for this image are labeled.

Fig. 4

Skin lesion region obtained after thresholding, blob labeling, and retaining the largest blob.

Fig. 5

Initial gradient vector flow snake boundary after initialization algorithm and cubic spline interpolation.

Fig. 6

Output of gradient vector flow snake deformation process during the first operation after 20 iterations. Snake boundaries are shown for every fifth iteration.

Fig. 7

Output of gradient vector flow snake deformation process during the second operation after 60 iterations. Snake boundaries are shown for every fifth iteration.

Fig. 8

Final skin lesion border after expanding gradient vector flow snake boundary from Figure 7 based on the normal direction along the snake boundary.

Fig. 9

Benign lesion image example showing borders determined manually and automatically superimposed onto the lesion. Key for the borders shown: baseline dermatologist manual border (white), gradient vector flow-based algorithm (red), Pagadala's method (green), manual border by second dermatologist (blue). Note that both human-and computer-based methods have problems with the same areas where the border is hazy.

Fig. 10

Example of the gradient vector flow snake algorithm converging to noise points around the skin lesion.