Citation

Abbas, A. A. and Xiaoning, Guo and Tan, Wooi Haw and Jalab, H. A. (2014) Combined spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel. Journal of Medical Systems, 38 (80). ISSN 0148-5598

Text Combined Spline and B-spline for an Improved Automatic Skin Lesion Segmentation in Dermoscopic Images Using Optimal Color Channel.pdf Restricted to Repository staff only Download (1MB)

Abstract

n a computerized image analysis environment, the irregularity of a lesion border has been used to differentiate between malignant melanoma and other pigmented skin lesions. The accuracy of the automated lesion border detection is a significant step towards accurate classification at a later stage. In this paper, we propose the use of a combined Spline and B-spline in order to enhance the quality of dermoscopic images before segmentation. In this paper, morphological operations and median filter were used first to remove noise from the original image during pre-processing. Then we proceeded to adjust image RGB values to the optimal color channel (green channel). The combined Spline and B-spline method was subsequently adopted to enhance the image before segmentation. The lesion segmentation was completed based on threshold value empirically obtained using the optimal color channel. Finally, morphological operations were utilized to merge the smaller regions with the main lesion region. Improvement on the average segmentation accuracy was observed in the experimental results conducted on 70 dermoscopic images. The average accuracy of segmentation achieved in this paper was 97.21 % (where, the average sensitivity and specificity were 94 % and 98.05 % respectively).

Item Type:	Article
Uncontrolled Keywords:	Image segmentation
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	17 Jul 2014 08:32
Last Modified:	19 Aug 2021 06:17
URII:	http://shdl.mmu.edu.my/id/eprint/5633

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