Research on Biomedical Engineering
Research on Biomedical Engineering
Original Article

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Machado, Marlene; Pereira, Jorge; Fonseca-Pinto, Rui

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Abstract Introduction: Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods: This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering) for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results: Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions: These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.


Curvelet Transform, Dermoscopy, Reticular pattern, Melanoma, Pattern recognition.


Anantha M, Moss R, Stoecker W. Detection of pigment network in dermatoscopy images using texture analysis. Computerized Medical Imaging and Graphics. 2004; 28(5):225-34. PMid:15249068.

Arroyo JG, Zapirain BG. Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis. Computers in Biology and Medicine. 2014; 44:144-57. PMid:24314859.

Barata C, Marques J, Rozeira J. A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Transactions on Biomedical Engineering. 2012; 59(10):2744-54. PMid:22829364.

Baumert J, Schmidt M, Giehl KA, Volkenandt M, Plewig G, Wendtner C, Schmid-Wendtner MH. Time trends in tumour thickness vary in subgroups: analysis of 6475 patients by age, tumour site and melanoma subtype. Melanoma Research. 2009; 19(1):24-30. PMid:19430403.

Benelli C, Roscetti E, Pozzo VD, Gasparini G, Cavicchini S. The dermoscopic versus the clinical diagnosis of melanoma. European Journal of Dermatology. 1999; 9(6):470-6. PMid:10491506.

Betta G, Di Leo G, Fabbrocini G, Paolillo A, Sommella P. Dermoscopic image-analysis system: Estimation of atypical pigment network and atypical vascular pattern. In: Proceedings of the IEEE International Workshop on Medical Measurement and Applications (MeMea 2006); 2006 Apr 20-21; Benevento, Italy. Washington: IEEE; 2006. p. 63-7.

Bleyer A, Viny A, Barr R. Cancer in 15- to 29-year-olds by primary site. The Oncologist. 2006; 11(6):590-601. PMid:16794238.

Calderbank A, Daubechies I, Sweldens W, Yeo BL. Lossless image compression using integer to integer Wavelet Transforms. In: Proceedings of the International Conference on Image Processing; 1997 Oct 26-29; Santa Barbara, CA, USA. Washington: IEEE; 1997. p. 596-9.

Candès E, Demanet L, Donoho DL, Ying L. Fast discrete curvelet transforms. Multiscale Modeling & Simulation. 2006; 5(3):861-99.

Candès J. Ridgelets: theory and applications [thesis]. Stanford: Department of Statistics, Stanford University; 1998.

Do MN, Vetterli M. The Finite Ridgelet Transform for image representation. IEEE Transactions on Image Processing. 2003; 12(1):16-28. PMid:18237876.

Downing A, Newton-Bishop J, Forman D. Recent trends in cutaneous malignant melanoma in the Yorkshire region of England; incidence, mortality and survival in relation to stage of disease, 1993-2003. British Journal of Cancer. 2006; 95(1):91-5. PMid:16755289.

Eltoukhy MM, Faye I, Samir BB. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Computerized Medical Imaging and Graphics. 2010; 34(4):269-76. PMid:20004076.

Fadili MJ, Starck JL. Curvelets and Ridgelets. In: Meyers R, editor. Encyclopedia of complexity and system science. New York: Springer; 2007.

Ferlay F, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. European Journal of Cancer. 2013; 49(6):1374-403. PMid:23485231.

Fleming M, Steger C, Zhang J, Gao J, Cognetta A, Pollak I, Dyer C. Techniques for a structural analysis of dermatoscopic imagery. Computerized Medical Imaging and Graphics. 1998; 22(5):375-89. PMid:9890182.

Fonseca-Pinto R, Caseiro P, Andrade A. Image Empirical Mode Decomposition (IEMD) in dermoscopic images: Artefact removal and lesion border detection. In: Proceedings of the Signal Processing Patterns Recognition and Applications (SPPRA 2010); 2010 Feb 17-19; Innsbruck, Austria. Canada: IASTED; 2010. p. 341-5.

Friedman RJ, Rigel DS, Kopf AW. Early detection of malignant melanoma: the role of the physician examination and self examination of the skin. CA: A Cancer Journal for Clinicians. 1985; 35(3):130-51. PMid:3921200.

Gardezi JS, Faye I, Eltoukhy MM. Analysis of mammogram images based on texture features of curvelet Sub-bands. In: Proceedings of the ICGIP 2013: Fifth International Conference on Graphic and Image Processing; 2013 Oct 26; Hong Kong, China. Washington: IEEE; 2014. v. 906924.

Grana C, Cucchiara R, Pellacani G, Seidenari S. Line detection and texture characterization of network patterns. In: Proceedings of the ICPR'06: 18th International Conference on Pattern Recognition; 2006; Hong Kong, China. Washington: IEEE; 2006. p. 275-8. v. 2.

Leo GD, Paolillo A, Sommella P, Fabbrocini G. Automatic diagnosis of melanoma: a software system based on the 7-point checklist. In: Proceedings of the HICSS 2010: 43rd Hawaii International Conference on System Sciences; 2010 Jan 5-8; Honolulu, HI, USA. Washington: IEEE; 2010. p. 1-10.

Li B, Meng MQH. Texture analysis for ulcer detection in capsule endoscopy images. Image and Vision Computing. 2009; 27(9):1336-42.

Liu W, Hill D, Gibbs A, Tempany M, Howe C, Borland R, Morand M, Kelly J. What features do patients notice that help to distinguish between benign pigmented lesions and melanomas? The ABCD(E) rule versus the seven-point checklist. Melanoma Research. 2005; 15(6):549-54. PMid:16314742.

Longo DL, Fauci AS, Kasper DL, Hauser SL, Jameson JL, Loscalzo J. Harrison’s principles of internal medicine. 18th ed. New York: McGraw-Hill; 2012.

Menzies SW, Ingvar C, McCarthy WH. A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma. Melanoma Research. 1996; 6(1):55-62. PMid:8640071.

Menzies SW. A method for the diagnosis of primary cutaneous melanoma using surface microscopy. Dermatologic Clinics. 2001; 19(2):299-305, viii. PMid:11556238.

Nikam SB, Agarwal S. Fingerprint liveness detection using curvet energy and cooccurrence signatures. In: Proceedings of the CGIV ’08: Fifth International Conference on Computer Graphics, Imaging and Visualization; 2008 Aug 26-28; Penang, Malaysia. Washington: IEEE; 2008. p. 217-22.

Paradiso MA, Carney T. Orientation discrimination as a function of stimulus eccentricity and size: Nasal/temporal retinal asymmetry. Vision Research. 1988; 28(8):867-74. PMid:3250082.

Rogers HW, Weinstock MA, Harris AR, Hinckley MR, Feldman SR, Fleischer AB, Coldiron BM. Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Archives of Dermatology. 2010; 146(3):283-7. PMid:20231499.

Sadeghi M, Razmara M, Lee TK, Atkins MS. A novel method for detection of pigment network in dermoscopic images using graphs. Computerized Medical Imaging and Graphics. 2011; 35(2):137-43. PMid:20724109.

Sant M, Allemani C, Santaquilani M, Knijn A, Marchesi F, Capocaccia R. EUROCARE-4. Survival of cancer patients diagnosed in 1995-1999. Results and commentary. European Journal of Cancer. 2009; 45(6):931-91. PMid:19171476.

Soyer HP, Smolle J, Leitinger G, Rieger E, Kerl H. Diagnostic reliability of dermoscopic criteria for detecting malignant melanoma. Dermatology. 1995; 190(1):25-30. PMid:7894091.

Starck JL, Candès E, Donoho DL. The curvelet transform for image denoising. IEEE Transactions on Image Processing. 2002; 11(6):670-84. PMid:18244665.

Stern RS. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Archives of Dermatology. 2010; 146(3):279-82. PMid:20231498.

Sumana IJ. Image retrieval using discrete curvelet [dissertation]. Melbourne: Gippsland School of Information Technology, Monash University; 2008.

Viola P, Jones MJ. Robust real-time face detection. International Journal of Computer Vision. 2004; 57(2):137-54.
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