First review on psoriasis severity risk stratification: An engineering perspective

Computers in Biology and Medicine

Volumn 63, Issue , 2015, Pages 52-63

  Shrivastava, Vimal K ^a   Londhe, Narendra D ^a,c   Sonawane, Rajendra S ^b   Suri, Jasjit S ^c,d  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^b Psoriasis Clinic and Research Centre   (India)

^c Global Biomedical Technologies Inc   (United States)

^d Nucl Eng Idaho State University   (United States)

Author keywords

Area; CAD system; Dermatology; Erythema; PASI; Psoriasis; Scaliness; Stratification; Thickness

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER AIDED DIAGNOSIS; DECISION SUPPORT SYSTEMS; DIAGNOSIS; FEATURE EXTRACTION; RISK ASSESSMENT; SKIN; THERMAL STRATIFICATION;

AREA; CAD SYSTEM; ERYTHEMA; PASI; PSORIASIS; SCALINESS; THICKNESS;

DERMATOLOGY;

ACCURACY; COLOR; DECISION SUPPORT SYSTEM; DISEASE SEVERITY; ERYTHEMA; HUMAN; PREDICTIVE VALUE; PRIORITY JOURNAL; PSORIASIS; PSORIASIS AREA AND SEVERITY INDEX; RELIABILITY; REVIEW; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SKIN REDNESS; SKIN SURFACE; SKINFOLD THICKNESS; SUPPORT VECTOR MACHINE; COMPUTER ASSISTED DIAGNOSIS; PROCEDURES; SEVERITY OF ILLNESS INDEX;

DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; PSORIASIS; RISK ASSESSMENT; SEVERITY OF ILLNESS INDEX;

EID: 84929623037     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2015.05.005     Document Type: Review

References (90)

1. National Psoriasis Foundation, Statistics. Available at: HYPERLINK, 2015 (accessed February 15, 2015). http://www.psoriasis.org/research/science-of-psoriasis/statistics.
2. International Federation of Psoriasis Associations, Facts about Psoriasis. Available at: HYPERLINK, 2015 (accessed February 15, 2015). http://www.worldpsoriasisday.com.
3. Chandran V., Raychaudhuri S.P. Geoepidemiology and environmental factors of psoriasis and psoriatic arthritis. J. Autoimmun. 2010, 34(3):J314-J321.
4. Persatuan Dermatologi Malaysia, Overview of Psoriasis in Malaysia. Available at: HYPERLINK, 2015 (accessed February 15, 2015). http://www.dermatology.org.my.
5. Dogra S., Yadav S. Psoriasis in India: prevalence and pattern. Indian J. Dermatol. Venereol. Leprol. 2010, 76(6):595.
6. Basko-Plluska J.L., Petronic-Rosic V. Psoriasis: epidemiology, natural history, and differential diagnosis. Psoriasis: Targets Ther. 2012, 2:67-76.
7. Murase J.E., Chan K.K., Garite T.J., Cooper D.M., Weinstein G.D. Hormonal effect on psoriasis in pregnancy and postpartum. Arch. Dermatol. 2005, 141(5):601-606.
8. Krueger G., Koo J., Lebwohl M., Menter A., Stern R.S., Rolstad T. The impact of psoriasis on quality of life: results of a 1998 National Psoriasis Foundation patient-membership. Arch. Dermatol. 2001, 137(3):280-284.
9. Langley R.G.B., Krueger G.G., Griffiths C.E.M. Psoriasis: epidemiology, clinical features, and quality of life. Ann. Rheum. Dis. 2005, 64(2):ii18-ii23.
10. Nestle F.O., Conrad C. Mechanisms of psoriasis. Drug Discov. Today: Dis. Mech. 2004, 1(3):315-319.
11. Olivier C., Robert P.D., Daihung D., et al. The risk of depression, anxiety, and suicidality in patients with psoriasis: a population-based cohort study. Arch. Dermatol. 2010, 146(8):891-895.
12. Hani A.F.M. Prakasa E. Nugroho H, Asirvadam V.S., Implementation of fuzzy c-means clustering for psoriasis assessment on lesion erythema, in: IEEE Symposium on Industrial Electronics and Applications (ISIEA)IEEE Symposium on. 2012, pp. 331-335.
13. Fadzil M.A., Fitriyah H., Prakasa E., Nugroho, H., Hussein S.H., Affandi A.M., Objective assessment of psoriasis lesion thickness for pasi scoring using 3d digital imaging, in: Proceedings of World Academy of Science, Engineering and Technology, vol. 63, 2010, pp. 109-115.
14. Camisa C. Handbook of Psoriasis 2004, Blackwell Publishing Ltd. second ed.
15. Henseler T. Genetics of psoriasis. Arch. Dermatol. Res. 1998, 290(9):463-476.
16. Morrow Thomas Evaluating new therapies for psoriasis. Manag. Care 2004, 13:34-40.
17. Puzenat E., Bronsard V., Prey S., et al. What are the best outcome measures for assessing plaque psoriasis severity? A systematic review of the literature. J. Eur. Acad. Dermatol. Venereol. 2010, 24(s2):10-16.
18. Chalmers R.J. Assessing psoriasis severity and outcomes for clinical trials and routine clinical practice. Dermatol. Clin. 2015, 33(1):57-71.
19. Feldman S.R., Krueger G.G. Psoriasis assessment tools in clinical trials. Ann. Rheum. Dis. 2005, 64(Suppl. 2):ii65-ii68.
20. Wozel J.S.G. The psoriasis area and severity index is the adequate criterion to define severity in chronic plaque-type psoriasis. Dermatology 2005, (210):194-199.
21. Fadzil M.H.A., Prakasa E., Asirvadam V.S., Nugroho H., Affandi A.M., Hussein S.H. 3D surface roughness measurement for scaliness scoring of psoriasis lesions. Comput. Biol. Med. 2013, 43(11):1987-2000.
22. Kreft S., Kreft M., Resman A., Marko P., Kreft K.Z. Computer-aided measurement of psoriatic lesion area in a multicenter clinical trial-comparison to physician's estimations. J. Dermatol. Sci. 2006, 44(1):21-27.
23. Shrivastava V.K., Londhe N.D., Sonawane R.S., Suri J.S. Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. Expert Syst. Appl. 2015.
24. Schäfer T. Epidemiology of psoriasis. Review and the German perspective. Dermatology (Basel, Switzerland) 2005, 212(4):327-337.
25. Kostović K., Pasić A. Phototherapy of psoriasis: review and update. Acta Dermatovenerol. Croat. 2003, 12(1):42-50.
26. Abbadi N.K., Dahir N.S., Dhalimi M.A., Restom H. Psoriasis detection using skin color and texture features. J. Comput. Sci. 2010, 6(6):648.
27. Xie J., Wang C. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 2011, 38(5):5809-5815.
28. Masood A., Ali Al-Jumaily A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 2013.
29. Maglogiannis I., Doukas C.N. Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 2009, 13(5):721-733.
30. Gupta M.A., Schork N.J., Gupta A.K., Kirkby S., Ellis C.N. Suicidal ideation in psoriasis. Int. J. Dermatol. 1993, 32(3):188-190.
31. Fadzil M.A., Ihtatho D., Affandi A.M., Hussein S.H. Area assessment of psoriasis lesions for PASI scoring. J. Med. Eng. Technol. 2009, 33(6):426-436.
32. Barati E., Saraee M., Mohammadi A., Adibi N., Ahmadzadeh M.R. A survey on utilization of data mining approaches for dermatological (skin) diseases prediction. J. Sel. Areas Health Inf. 2011, 2(3):1-11.
33. Taur J.S. Neuro-fuzzy approach to the segmentation of psoriasis images. J. VLSI Signal Process. Syst. Signal Image Video Technol. 2003, 35(1):19-27.
34. Taur J.S., Lee G.H., Tao C.W., Chen C.C., Yang C.W. Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2006, 36(2):390-402.
35. Juang L.H., Wu M.N. Psoriasis image identification using k-means clustering with morphological processing. Measurement 2011, 44(5):895-905.
36. Bidaki E.Z., Zargari F., Mansouri P., Malekian A., Najafizade P. Lesion area assessment in psoriasis patients. J. Med. Eng. Technol. 2011, 35(3-4):154-160.
37. Lu J., Kazmierczak E., Manton J.H., Sinclair R. Automatic segmentation of scaling in 2-D psoriasis skin images. IEEE Trans. Med. Imaging 2013, 32(4):719-730.
38. Lu J., Kazmierczak E., Manton J.H., Sinclair R., Automatic scoring of erythema and scaling severity in psoriasis diagnosis, in: AI 2012: Advances in Artificial Intelligence, Springer Berlin Heidelberg, 2012, pp. 73-84.
39. Lu J., Kazmierczak E., Manton J.H., Sinclair R., A quantitative technique for assessing the change in severity over time in psoriatic lesions using computer aided image analysis, in: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, July, pp. 2380-2383.
40. Lee T., Ng V., Gallagher R., Coldman A., McLean D. Dull-razor: a software approach to hair removal from images. Comput. Biol. Med. 1997, 27(6):533-543.
41. Kalyan K., Jakhia B., Lele R.D., Joshi M., Chowdhary A. Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images. Adv. Bioinf. 2014 2014.
42. Acharya U.R., Sree S.V., Kulshreshtha S., et al. GyneScan: an improved online paradigm for screening of Ovarian Cancer via tissue characterization. Technol. Cancer Res. Treat. 2014, 13(6):529-539.
43. Loizou C.P., Pantziaris M., Pattichis M.S., Kyriacou E., Pattichis C.S. Ultrasound image texture analysis of the intima and media layers of the common carotid artery and its correlation with age and gender. Comput. Med. Imaging Graph. 2009, 33(4):317-324.
44. Yu H., Caldwell C., Mah K., Mozeg D., Coregistered F.D.G. PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans. Med. Imaging 2009, 28(3):374-383.
45. Loizou C.P., Kasparis T., Polyviou M. Evaluation of wound healing process based on texture image analysis. J. Biomed. Graphics Comput. 2013, 3(3):p1.
46. Mirmehdi M., Xie X., Suri J. Handbook of Texture Analysis 2009, Imperial College Press.
47. Celebi M.E., Kingravi H.A., Uddin B., et al. A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 2007, 31(6):362-373.
48. Pereira S.M., Frade M.A.C., Rangayyan R.M., Azevedo-Marques P.M. Classification of color images of dermatological ulcers. IEEE J. Biomed. Health Inf. 2013, 17(1):136-142.
49. Chang W.Y., Huang A., Yang C.Y., et al. Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PLoS One 2013, 8(11):e76212.
50. Maglogiannis I.G., Zafiropoulos E.P. Characterization of digital medical images utilizing support vector machines. BMC Med. Inf. Decis. Making 2004, 4(1):4.
51. Sree S.V., Ng E.Y.K., Acharya U.R. Data mining approach to evaluating the use of skin surface electropotentials for breast cancer detection. Technol. Cancer Res. Treat. 2010, 9(1):95-105.
52. Mikolajczyk K., Schmid C. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(10):1615-1630.
53. Whitney A.W. A direct method of nonparametric measurement selection. IEEE Trans. Comput. 1971, 20(9):1100-1103.
54. Marill T., Green D.M. On the effectiveness of receptors in recognition systems. IEEE Trans. Inf. Theory 1963, 9(1):11-17.
55. Pudil P., Novovičová J., Kittler J. Floating search methods in feature selection. Pattern Recognit. Lett. 1994, 15(11):1119-1125.
56. Li C., Lin X., Hui C., Lam K.M., Zhang S. Computer-aided diagnosis for distinguishing pancreatic mucinous cystic neoplasms from serous oligocystic adenomas in spectral CT images. Technol. Cancer Res. Treat. 2014, 1533034614563013.
57. Kononenko I., Simec E. Induction of decision trees using RELIEFF, in: Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence 1995, 199-220. Springer, Vienna.
58. Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 1994, 5(4):537-550.
59. Hall M.A. Correlation-based Feature Selection for Machine Learning 1999, Doctoral Dissertation, The University of Waikato.
60. Handels H., Roß T., Kreusch J., Wolff H.H., Poeppl S.J. Feature selection for optimized skin tumor recognition using genetic algorithms. Artif. Intell. Med. 1999, 16(3):283-297.
61. Nakariyakul S., Casasent D.P., Improved forward floating election algorithm for feature subset selection, in: International Conference on Wavelet Analysis and Pattern Recognition, 2008, ICWAPR[U+05F3]08, vol. 2, 2008, pp. 793-798.
62. Vapnik V. Statistical Learning Theory 1998, Wiley, New York, NY.
63. Cross S.S., Harrison R.F., Kennedy R.L. Introduction to neural networks. Lancet 1995, 346(8982):1075-1079.
64. Cover T., Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13(1):21-27.
65. Safavian S.R., Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 1991, 21(3):660-674.
66. Harrell F.E., Lee K.L., Califf R.M., Pryor D.B., Rosati R.A. Regression modelling strategies for improved prognostic prediction. Stat. Med. 1984, 3(2):143-152.
67. Friedman N., Geiger D., Goldszmidt M. Bayesian network classifiers. Mach. Learn. 1997, 29(2-3):131-163.
68. Krafft C., Shapoval L., Sobottka S.B., Schackert G., Salzer R. Identification of primary tumors of brain metastases by infrared spectroscopic imaging and linear discriminant analysis. Technol. Cancer Res. Treat. 2006, 5(3):291-298.
69. Glaister J., Amelard R., Wong A., Clausi D.A. Msim: multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis. IEEE Trans. Biomed. Eng. 2013, 60(7):1873-1883.
70. Dreiseitl S., Ohno-Machado L., Kittler H., Vinterbo S., Billhardt H., Binder M. A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J. Biomed. Inf. 2001, 34(1):28-36.
71. Rahman M.M., Bhattacharya P. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Comput. Med. Imaging Graph. 2010, 34(6):479-486.
72. Messadi M., Bessaid A., Taleb-Ahmed A. Extraction of specific parameters for skin tumour classification. J. Med. Eng. Technol. 2009, 33(4):288-295.
73. Messadi M., Cherifi H., Bessaid A. Segmentation and ABCD rule extraction for skin tumors classification. J. Convergence Inf. Technol. 2014, 9:2.
74. Barzegari M., Ghaninezhad H., Mansoori P., Taheri A., Naraghi Z.S., Asgari M. Computer-aided dermoscopy for diagnosis of melanoma. BMC Dermatol. 2005, 5(1):8.
75. Ruiz D., Berenguer V., Soriano A., Sánchez B. A decision support system for the diagnosis of melanoma: a comparative approach. Expert Syst. Appl. 2011, 38(12):15217-15223.
76. Maglogiannis I., Pavlopoulos S., Koutsouris D. An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans. Inf. Technol. Biomed. 2005, 9(1):86-98.
77. Chang C.L., Chen C.H. Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst. Appl. 2009, 36(2):4035-4041.
78. Kia S., Setayeshi S., Shamsaei M., Kia M. Computer-aided diagnosis (CAD) of the skin disease based on an intelligent classification of sonogram using neural network. Neural Comput. Appl. 2013, 22(6):1049-1062.
79. Sáez A., Serrano C., Acha B. Model-based classification methods of global patterns in dermoscopic images. IEEE Trans. Med. Imaging 2014, 33(5):1137-1147.
80. Cula O.G., Dana K.J., Murphy F.P., Rao B.K. Bidirectional imaging and modeling of skin texture. IEEE Trans. Biomed. Eng. 2004, 1(12):2148-2159.
81. Fikrle T., Pizinger K. Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi. J. Eur. Acad. Dermatol. Venereol. 2007, 21(1):48-55.
82. Celebi M.E., Zornberg A. Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE Syst. J. 2014, 8(3):980-984.
83. Rodriguez J.D., Perez A., Lozano J.A. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32(3):569-575.
84. Wilke M. An iterative jackknife approach for assessing reliability and power of fMRI group analyses. PLoS One 2012, 7(4):e35578.
85. Guo P., Luo Y., Mai G. Gene expression profile based classification models of psoriasis. Genomics 2014, 103(1):48-55.
86. Fidalgo Barata A., Celebi E., Marques J. Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health Inf. 2014, 99:1.
87. Acharya U.R., Faust O., Alvin A.P.C., et al. Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput. Methods Programs Biomed. 2013, 110(1):66-75.
88. Acharya U.R., Sree S.V., Ribeiro R. Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med. Phys. 2012, 39(7):4255-4264.
89. Acharya U.R., Molinari F., Sree S.V., Chattopadhyay S., Ng K.H., Suri J.S. Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 2012, 7(4):401-408.
90. Acharya U.R., Molinari F., Sree S.V., et al. Ovarian tissue characterization in ultrasound a review. Technol. Cancer Res. Treat. 2014, 1533034614547445.