Title :
Automatic Segmentation of Skin Lesion Images using Evolutionary Strategy
Author :
Situ, Ning ; Yuan, Xiaojing ; Zouridakis, George ; Mullani, Nizar
Author_Institution :
Univ. of Houston, Houston
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
Malignant melanoma has a good prognosis if treated early. Accurate skin lesion segmentation from the background skin is important not only because the shape feature can be directly derived from the process, but also because it can provide a scope for texture analysis. In this paper, we propose an evolutionary strategy based segmentation algorithm to identify the lesion area by an ellipse. It can detect the lesion automatically without setting parameters manually. The method is validated by experiments and comparisons with manually segmentation by an expert and algorithms developed by M. Doshi, et al. (2004).
Keywords :
bioluminescence; biomedical optical imaging; cancer; evolutionary computation; image segmentation; image texture; medical image processing; skin; tumours; automatic segmentation; cross-polarization epiluminescence microscopy; evolutionary strategy; malignant melanoma; shape feature; skin lesion images; texture analysis; transillumination epiluminescence microscopy; Cancer detection; Computer science; Fuzzy systems; Image segmentation; Lesions; Malignant tumors; Microscopy; Pigmentation; Shape; Skin cancer; Evolutionary Strategy; biomedical application; fitness function; image segmentation; skin lesion;
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2007.4379575