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. 2016 Sep 19;5(1):1603.
doi: 10.1186/s40064-016-3211-4. eCollection 2016.

Segmentation of skin lesion using Cohen-Daubechies-Feauveau biorthogonal wavelet

Shehzad Khalid  1 , Uzma Jamil  2 , Kashif Saleem  1 , M Usman Akram  3 , Waleed Manzoor  1 , Waqas Ahmed  1 , Amina Sohail  1
Affiliations

Segmentation of skin lesion using Cohen-Daubechies-Feauveau biorthogonal wavelet

Shehzad Khalid et al. Springerplus. .

Abstract

This paper presents a novel technique for segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological operations. The acquired dermoscopic images may include artifacts inform of gel, dense hairs and water bubble which make accurate segmentation more challenging. We have also embodied an efficient approach for artifacts removal and hair inpainting, to enhance the overall segmentation results. In proposed research, color space is also analyzed and selection of blue channel for lesion segmentation have confirmed better performance than techniques which utilizes gray scale conversion. We tackle the problem by finding the most suitable mother wavelet for skin lesion segmentation. The performance achieved with 'bior6.8' Cohen-Daubechies-Feauveau biorthogonal wavelet is found to be superior as compared to other wavelet family. The proposed methodology achieves 93.87 % accuracy on dermoscopic images of PH2 dataset acquired at Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

Keywords: Border detection; Dermoscopy; Image enhancement; Image segmentation; Skin cancer.

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Figures

Fig. 1
Fig. 1
Computer added diagnostic steps
Fig. 2
Fig. 2
CAD process and segmentation break-up
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Fig. 3
Segmentation classification
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Fig. 4
Proposed methodology
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Fig. 5
Example images from PH data-set
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Fig. 6
Results of color enhancement techniques: a original image, b blue color, c illumination transformation, d highest entropy
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Fig. 7
a Original image, b hair enhancement, c hair segmentation, d hair inpainting
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Fig. 8
a Image with dark corners, b binary mask
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Fig. 9
Wavelet transformation
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Fig. 10
Wavelet transformation on dermoscopic image: a original image, b wavelet transformation
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Fig. 11
Steps of the proposed dermoscopic image segmentation algorithm a original image, b active contour, c blue channel, d wavelet transformation, e segmented image after post-processing, f dermoscopic image with segmentation
Fig. 12
Fig. 12
a Original images, b images with segmentation
See this image and copyright information in PMC

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