Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma

The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, there is a growing interest among clinicians regarding the potential diagnostic utility of computerised image analysis. Recognising that there exist significant shortcomings in currently available algorithms, we are motivated to investigate the utility of lacunarity, a simple statistical measure previously used in geology and other fields for the analysis of fractal and multi-scaled images, in the automated assessment of melanocytic naevi and melanoma. Digitised dermoscopic images of 111 benign melanocytic naevi, 99 dysplastic naevi and 102 melanomas were obtained over the period 2003 to 2008, and subject to lacunarity analysis. We found the lacunarity algorithm could accurately distinguish melanoma from benign melanocytic naevi or non-melanoma without introducing many of the limitations associated with other previously reported diagnostic algorithms. Lacunarity analysis suggests an ordering of irregularity in melanocytic lesions, and we suggest the clinical application of this ordering may have utility in the naked-eye dermoscopic diagnosis of early melanoma.

[1]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[2]  B. Mandelbrot,et al.  Geometric Implementation of Hypercubic Lattices with Noninteger Dimensionality by Use of Low Lacunarity Fractal Lattices , 1983 .

[3]  Z. R. Yang,et al.  A suggested lacunarity expression for Sierpinski carpets , 1986 .

[4]  N Cascinelli,et al.  A possible new tool for clinical diagnosis of melanoma: the computer. , 1987, Journal of the American Academy of Dermatology.

[5]  C. Allain,et al.  Characterizing the lacunarity of random and deterministic fractal sets. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[6]  M. Binder,et al.  Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study , 1994, The British journal of dermatology.

[7]  W Abmayr,et al.  Evaluation of different image acquisition techniques for a computer vision system in the diagnosis of malignant melanoma. , 1994, Journal of the American Academy of Dermatology.

[8]  A. Green,et al.  Computer image analysis in the diagnosis of melanoma. , 1994, Journal of the American Academy of Dermatology.

[9]  W. Hargrove,et al.  Lacunarity analysis: A general technique for the analysis of spatial patterns. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  H. Kittler,et al.  Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network , 1998, Melanoma research.

[11]  John F. Weishampel,et al.  Mesoscale changes in textural pattern of 'intact' Peruvian rainforests (1970s-1980s) , 1998 .

[12]  M A Weinstock,et al.  Thorough skin examination for the early detection of melanoma. , 1999, American journal of preventive medicine.

[13]  P Barbini,et al.  Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma: a new quantitative semiology. , 1999, Archives of dermatology.

[14]  M S Ernstoff.  Melanoma. Screening and education. , 2000, Clinics in plastic surgery.

[15]  P Bauer,et al.  Digital epiluminescence microscopy: usefulness in the differential diagnosis of cutaneous pigmentary lesions. A statistical comparison between visual and computer inspection , 2000, Melanoma research.

[16]  M. Oliviero,et al.  Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study. , 2001, Journal of the American Academy of Dermatology.

[17]  H P Soyer,et al.  Dermoscopy of pigmented skin lesions--a valuable tool for early diagnosis of melanoma. , 2001, The Lancet. Oncology.

[18]  D. Piccolo,et al.  Clinical and Laboratory Investigations Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study , 2002 .

[19]  G. Henebry,et al.  Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis. , 2002, Medical engineering & physics.

[20]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[21]  H. Kittler,et al.  Diagnostic accuracy of dermoscopy. , 2002, The Lancet. Oncology.

[22]  P. Barbini,et al.  Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study. , 2002, The Journal of investigative dermatology.

[23]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[24]  P Altmeyer,et al.  Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer‐aided analysis of data from pigmented skin lesions using digital dermoscopy , 2003, The British journal of dermatology.

[25]  G Rassner,et al.  Clinical and Laboratory Investigations Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions , 2004 .

[26]  Giuseppe Argenziano,et al.  Three-point checklist of dermoscopy. A new screening method for early detection of melanoma. , 2004, Dermatology.

[27]  Ralph Braun,et al.  The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. , 2005, Archives of dermatology.

[28]  Lorenzo Scalise,et al.  Fractal characterisation of boundary irregularity in skin pigmented lesions , 2005, Medical and Biological Engineering and Computing.

[29]  Aglaia G. Manousaki,et al.  Use of color texture in determining the nature of melanocytic skin lesions - a qualitative and quantitative approach , 2006, Comput. Biol. Medicine.

[30]  Evgenia I. Tsompanaki,et al.  A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit. A preliminary report , 2006, International journal of dermatology.

[31]  Milind Rajadhyaksha,et al.  Differences between polarized light dermoscopy and immersion contact dermoscopy for the evaluation of skin lesions. , 2007, Archives of dermatology.

[32]  T Fikrle,et al.  Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi , 2007, Journal of the European Academy of Dermatology and Venereology : JEADV.

[33]  Hans Peter Soyer,et al.  Multispectral Image Analysis , 2007 .

[34]  Rainer Hofmann-Wellenhof,et al.  Color Atlas of Melanocytic Lesions of the Skin , 2008 .

[35]  R. Hofmann-Wellenhof,et al.  Nevus type in dermoscopy is related to skin type in white persons. , 2007, Archives of dermatology.