Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

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

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

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

[4]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

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

[6]  Gustavo Marrero Callicó,et al.  Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging , 2019, Sensors.

[7]  E. Claridge,et al.  Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions , 2002, The British journal of dermatology.

[8]  Helen Swede,et al.  Paired comparison of the sensitivity and specificity of multispectral digital skin lesion analysis and reflectance confocal microscopy in the detection of melanoma in vivo: A cross-sectional study. , 2016, Journal of the American Academy of Dermatology.

[9]  H. Koga,et al.  Modification of a melanoma discrimination index derived from hyperspectral data: a clinical trial conducted in 2 centers between March 2011 and December 2013 , 2015, 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.

[10]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[11]  Renato Marchesini,et al.  Automated melanoma detection with a novel multispectral imaging system: results of a prospective study , 2005, Physics in medicine and biology.

[12]  Bogdan Zagajewski,et al.  Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images , 2017 .

[13]  Guolan Lu,et al.  Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis , 2016, SPIE Medical Imaging.

[14]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[15]  J. Lear,et al.  Non-melanoma skin cancer , 2010, The Lancet.

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

[17]  Takashi Nagaoka,et al.  A possible melanoma discrimination index based on hyperspectral data: a pilot study , 2012, 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.

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

[19]  Philipp Probst,et al.  Hyperparameters and tuning strategies for random forest , 2018, WIREs Data Mining Knowl. Discov..

[20]  Mahdi Hasanipanah,et al.  Airblast prediction through a hybrid genetic algorithm-ANN model , 2018, Neural Computing and Applications.

[21]  J J Stamnes,et al.  Optical detection and monitoring of pigmented skin lesions. , 2017, Biomedical optics express.

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

[23]  Roberto Sarmiento,et al.  Detecting brain tumor in pathological slides using hyperspectral imaging. , 2018, Biomedical optics express.

[24]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

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

[26]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

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

[28]  M. Mihm,et al.  The performance of MelaFind: a prospective multicenter study. , 2011, Archives of dermatology.

[29]  Christine Fink,et al.  Diagnostic performance of the MelaFind device in a real‐life clinical setting , 2017, Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG.

[30]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[31]  Guang-Zhong Yang,et al.  An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation , 2018, Sensors.

[32]  John Muschelli,et al.  ROC and AUC with a Binary Predictor: a Potentially Misleading Metric , 2019, Journal of Classification.

[33]  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.

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

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

[36]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Baowei Fei,et al.  In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer , 2019, Cancers.

[38]  Stefan Fritsch,et al.  neuralnet: Training of Neural Networks , 2010, R J..

[39]  Toshiaki Saida,et al.  Hyperspectroscopic screening of melanoma on acral volar skin , 2013, 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.