PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma

The repair of hair-occluded information is one of the key problems for the precise segmentation and analysis of the skin malignant melanoma image with hairs. Aimed at dermoscopy images of pigmented skin lesions, an unsupervised repair algorithm for the hair-occluded information is proposed in this paper. This algorithm includes three steps: first, the melanoma image with hairs are enhanced by morphologic closing-based top-hat operator and then segmented through statistic threshold; second, the hairs are extracted based on the elongate of connected region; third, the hair-occluded information is repaired by the PDE-based image inpainting. As a matter of fact, with the morphologic closing-based top-hat operator both strong and weak hairs can be enhanced simultaneously, and the elongate state of band-like connected region can be correctly described by the elongate function proposed in this paper so as to measure the hair effectively. Therefore, the unsupervised repair problem of the hair-occluded information can be resolved very well through combining the hair extracting with the image inpainting technology. The experiment results show that the repaired images can satisfy the requirement of medical diagnosis by the proposed algorithm and the segmentation veracity is effectively improved after repairing the hair-occluded information.

[1]  T Lee,et al.  Dullrazor®: A software approach to hair removal from images , 1997, Comput. Biol. Medicine.

[2]  Randy H. Moss,et al.  Fast and accurate border detection in dermoscopy images using statistical region merging , 2007, SPIE Medical Imaging.

[3]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[4]  W V Stoecker,et al.  Nondermatoscopic digital imaging of pigmented lesions , 1995, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[5]  Timothy S. Newman,et al.  New RHT-Based Ellipsoid Recovery Method , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  David B. Cooper,et al.  Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  A. Jemal,et al.  Cancer Statistics, 2006 , 2006, CA: a cancer journal for clinicians.

[8]  R. Naguib,et al.  Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management , 2000 .

[9]  P. Schmid.  Segmentation of digitized dermatoscopic images by two-dimensional color clustering , 1999, IEEE Transactions on Medical Imaging.

[10]  Wu Jiying and Ruan Qiuqi.  A Curvature-Driven Image Inpainting Model Based on Helmholtz Vorticity Equation , 2007 .

[11]  Zeng Jie.  The Application of Hough Transform in the Detection of Exponent Function Curve , 2005 .

[12]  Peng Qi-cong.  A Survey on Digital Image Inpainting , 2007 .

[13]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Rita Cucchiara,et al.  A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions , 2003, IEEE Transactions on Medical Imaging.

[15]  Xie Feng-ying.  Road Extraction from High-Resolution Remotely Sensed Image in Dual Space , 2006 .

[16]  Clement T. Yu,et al.  Segmentation of skin cancer images , 1999, Image Vis. Comput..

[17]  A. Kopf,et al.  Total-body photographs of dysplastic nevi. , 1988, Archives of dermatology.

[18]  Benyamin Kusumoputro,et al.  Neural network diagnosis of malignant skin cancers using principal component analysis as a preprocessor , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[19]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[20]  M.S. Bouhlel,et al.  A New Automatic Approach for Edge Detection of Skin Lesion Images , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[21]  Renato Marchesini,et al.  Automated melanoma detection with a novel multispectral imaging system: results of a prospective study , 2005, Physics in medicine and biology.