Dermatologic Hyperspectral Imaging System for Skin Cancer Diagnosis Assistance

This paper presents the development of a dermatological acquisition system based on hyperspectral (HS) imaging for the assistance in the diagnosis of pigmented skin lesions (PSLs). The developed system is able to capture HS images of 50×50 pixels and 125 spectral bands in the VNIR (Visual and Near Infrared) region between 450 and 950 nm, using a cold light halogen illumination device. The system is able to capture images of a size of 12×12 mm in less than 1 second. Employing this system, a preliminary database of 49 HS images of PSLs from 36 patients was generated. The data was labeled in four different classes and classified using a supervised machine learning method optimized by means of a genetic algorithm. The results obtained in these preliminary experiments demonstrate the potential of the developed system to perform a rapid and accurate assistance in the skin cancer diagnosis task during clinical routine practice.

[1]  G. Argenziano,et al.  The impact of dermoscopy on melanoma detection in the practice of dermatologists in Europe: results of a pan‐European survey , 2017, Journal of the European Academy of Dermatology and Venereology : JEADV.

[2]  Guang-Zhong Yang,et al.  In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection , 2019, IEEE Access.

[3]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[4]  Jeremy S Bordeaux,et al.  Early detection of melanoma: reviewing the ABCDEs. , 2015, Journal of the American Academy of Dermatology.

[5]  Nitesh V. Chawla,et al.  Correspondence SVMs Modeling for Highly Imbalanced Classification , 2009 .

[6]  Di Wu,et al.  Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .

[7]  Tim K. Lee,et al.  Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness , 2015, Medical Imaging.

[8]  Fred Godtliebsen,et al.  Recent advances in hyperspectral imaging for melanoma detection , 2019, WIREs Computational Statistics.

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

[10]  Chih-Hung Wu,et al.  A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy , 2007, Expert Syst. Appl..

[11]  A. Marghoob,et al.  High-dynamic-range dermoscopy imaging and diagnosis of hypopigmented skin cancers. , 2015, JAMA dermatology.

[12]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[13]  Francisco J. Rodríguez,et al.  Genetic Algorithms , 2018, Handbook of Heuristics.

[14]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Diego Cabrera,et al.  Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .

[16]  Mia K Markey,et al.  Segmentation of diffuse reflectance hyperspectral datasets with noise for detection of Melanoma , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[18]  Vandana Jagtap,et al.  Computer Aided Melanoma Skin Cancer Detection Using Image Processing , 2015 .

[19]  Guang-Zhong Yang,et al.  Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations , 2018, PloS one.

[20]  Valery Naranjo,et al.  Multivariate classification of fourier transform infrared hyperspectral images of skin cancer cells , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[21]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[22]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[23]  Dongrong Xu,et al.  Review of spectral imaging technology in biomedical engineering: achievements and challenges , 2013, Journal of biomedical optics.

[24]  Y. Kosugi,et al.  Cancer detection using infrared hyperspectral imaging , 2011, Cancer science.