Ridge and furrow pattern classification for acral lentiginous melanoma using dermoscopic images

Abstract Background/purpose The development of an automatic diagnostic algorithm using characteristics of dermoscopic findings in acral lentiginous melanoma (ALM) has been slow due to the rarity of melanoma in non-Caucasian populations. In this study, we present an automatic algorithm that can distinguish the “furrow” and “ridge” patterns of pigmentation on the palm and foot, and report its usefulness for the detection of ALM. Methods To distinguish between ALM and nevus, the proposed image analysis is applied. From a dermoscopic image, edges having the steepest ascent or descent are detected through Gaussian derivative filtering. The widths between edges are then measured and the brightness of each stripe is tagged. The dark area is tagged as black and the bright area is tagged as white. The ratio of widths of dark to bright is calculated at each stripe pair and the histogram of the width ratio in the dermoscopic image is generated. Results A total of 297 dermoscopic images confirmed by histopathologic diagnoses are classified. All of the melanoma dermoscopic images were classified correctly using the proposed algorithm, while only one nevus image was misclassified. The proposed method achieved a sensitivity of 100%, a specificity of 99.1%, an accuracy of 99.7%, and a similarity of 99.7%. Conclusion In this study, we propose a novel automatic algorithm that can precisely distinguish the “furrow” and “ridge” patterns of pigmentation on dermoscopic images using the width ratio of dark and bright patterns. It is expected that the proposed algorithm will contribute to the early diagnosis of ALM.

[1]  Domenico Piccolo,et al.  Computer-automated ABCD versus dermatologists with different degrees of experience in dermoscopy , 2014, European Journal of Dermatology.

[2]  Rui Fonseca-Pinto,et al.  Classification of reticular pattern and streaks in dermoscopic images based on texture analysis , 2015, Journal of medical imaging.

[3]  Z. She,et al.  Combination of features from skin pattern and ABCD analysis for lesion classification , 2007, 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.

[4]  Qaisar Abbas,et al.  Pattern classification of dermoscopy images: A perceptually uniform model , 2013, Pattern Recognit..

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

[6]  H. Koga,et al.  Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin. , 2008, The Journal of investigative dermatology.

[7]  H. Tagami,et al.  Improvement in survival rate of patients with acral melanoma observed in the past 22 years in Sendai, Japan , 1993, Clinical and experimental dermatology.

[8]  Josep Malvehy,et al.  Atlas of Dermoscopy , 2004 .

[9]  Toshiaki Saida,et al.  Significance of dermoscopic patterns in detecting malignant melanoma on acral volar skin: results of a multicenter study in Japan. , 2004, Archives of dermatology.

[10]  S Touzet,et al.  Dermoscopic features of acral lentiginous melanoma in a large series of 110 cases in a white population , 2009, The British journal of dermatology.

[11]  H. Koga,et al.  Key points in dermoscopic differentiation between early acral melanoma and acral nevus , 2011, The Journal of dermatology.

[12]  Begoña García Zapirain,et al.  Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms , 2011, Comput. Biol. Medicine.

[13]  T. Ruzicka,et al.  Plantar malignant melanoma – a challenge for early recognition , 2000, Melanoma research.

[14]  Reda Kasmi,et al.  Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule , 2016, IET Image Process..

[15]  Giordana Dell'Eva,et al.  Digital dermoscopy in clinical practise: a three‐centre analysis , 2007, 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.

[16]  A. Ormerod,et al.  Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma , 2009, The British journal of dermatology.

[17]  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 .

[18]  H. Iyatomi,et al.  Reply to ‘Digital dermoscopy analysis and internet‐based program for discrimination of pigmented skin lesion dermoscopic images’ , 2005, The British journal of dermatology.

[19]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[20]  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.

[21]  T. Saida.  Malignant Melanoma in Situ on the Sole of the Foot: Its Clinical and Histopathologic Characteristics , 1989, The American Journal of dermatopathology.

[22]  Bennett Dr,et al.  The effect of misdiagnosis and delay in diagnosis on clinical outcome in melanomas of the foot. , 1994 .

[23]  Clay J Cockerell,et al.  Biopsy of the pigmented lesion--when and how. , 2008, Journal of the American Academy of Dermatology.

[24]  S. W. Ham,et al.  Pre-Processing for Determining Acral Lentiginous Melanoma(ALM) , 2015 .

[25]  A. Jerant,et al.  Early detection and treatment of skin cancer. , 2000, American family physician.

[26]  R. Johr.  Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. , 2002, Clinics in dermatology.

[27]  B. Stewart,et al.  World cancer report 2014. , 2014 .

[28]  Toshiaki Saida,et al.  Automated evaluation system of dermoscopic images of longitudinal melanonychia: Proposition of a discrimination index for detecting early nail apparatus melanoma , 2014, The Journal of dermatology.

[29]  Kee Yang Chung,et al.  Treatment and Outcomes of Melanoma in Acral Location in Korean Patients , 2010, Yonsei medical journal.

[30]  Ilias Maglogiannis,et al.  Computational vision systems for the detection of malignant melanoma. , 2006, Oncology reports.

[31]  E. Holly,et al.  Incidence of cutaneous melanoma among non-Hispanic Whites, Hispanics, Asians, and Blacks: an analysis of California Cancer Registry data, 1988-93 , 1997, Cancer Causes & Control.