Calibration and segmentation of skin areas in hyperspectral imaging for the needs of dermatology

BioMedical Engineering OnLine

Robert Koprowski²  Sławomir Wilczyński¹  Zygmunt Wróbel²  Barbara Błońska-Fajfrowska¹ 

[1] Department of Basic Biomedical Science, School of Pharmacy, Medical University of Silesia in Katowice, ul, Kasztanowa 3, Sosnowiec 41-200, Poland

[2] Department of Biomedical Computer Systems, Faculty of Computer Science and Materials Science, University of Silesia, Institute of Computer Science, ul, Będzińska 39, Sosnowiec 41-200, Poland

关键词: Calibration;    Dermatology;    Segmentation;    Measurement automation;    Image processing;    Hyperspectral imaging;   

Others  :  1084562
DOI  :  10.1186/1475-925X-13-113

 received in 2014-07-02, accepted in 2014-07-25,  发布年份 2014

【 摘 要 】

Introduction

Among the currently known imaging methods, there exists hyperspectral imaging. This imaging fills the gap in visible light imaging with conventional, known devices that use classical CCDs. A major problem in the study of the skin is its segmentation and proper calibration of the results obtained. For this purpose, a dedicated automatic image analysis algorithm is proposed by the paper’s authors.

Material and method

The developed algorithm was tested on data acquired with the Specim camera. Images were related to different body areas of healthy patients. The resulting data were anonymized and stored in the output format, source dat (ENVI File) and raw. The frequency λ of the data obtained ranged from 397 to 1030 nm. Each image was recorded every 0.79 nm, which in total gave 800 2D images for each subject. A total of 36'000 2D images in dat format and the same number of images in the raw format were obtained for 45 full hyperspectral measurement sessions. As part of the paper, an image analysis algorithm using known analysis methods as well as new ones developed by the authors was proposed. Among others, filtration with a median filter, the Canny filter, conditional opening and closing operations and spectral analysis were used. The algorithm was implemented in Matlab and C and is used in practice.

Results

The proposed method enables accurate segmentation for 36’000 measured 2D images at the level of 7.8%. Segmentation is carried out fully automatically based on the reference ray spectrum. In addition, brightness calibration of individual 2D images is performed for the subsequent wavelengths. For a few segmented areas, the analysis time using Intel Core i5 CPU RAM M460@2.5GHz 4GB does not exceed 10 s.

Conclusions

The obtained results confirm the usefulness of the applied method for image analysis and processing in dermatological practice. In particular, it is useful in the quantitative evaluation of skin lesions. Such analysis can be performed fully automatically without operator’s intervention.

【 授权许可】

   
2014 Koprowski et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

【 参考文献 】

• [1]Li B, Beveridge P, O’Hare WT, Islam M: The age estimation of blood stains up to 30 days old using visible wavelength hyperspectral image analysis and linear discriminant analysis. Sci Justice 2013, 53(3):270-277.
• [2]Gao BC, Chen W: Multispectral decomposition for the removal of out-of-band effects of visible/infrared imaging radiometer suite visible and near-infrared bands. Appl Opt 2012, 51(18):4078-4086.
• [3]Zhu L, Chen Z, Wang J, Ding J, Yu Y, Li J, Xiao N, Jiang L, Zheng Y, Rimmington GM: Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. Mar Pollut Bull 2014, S0025-326X(14):00426-3.
• [4]Mortimer M, Gogos A, Bartolomé N, Kahru A, Bucheli TD, Slaveykova VI: Potential of hyperspectral imaging microscopy for semi-quantitative analysis of nanoparticle uptake by protozoa. Environ Sci Technol 2014. In press
• [5]Goyal A, Myers T, Wang CA, Kelly M, Tyrrell B, Gokden B, Sanchez A, Turner G, Capasso F: Active hyperspectral imaging using a quantum cascade laser (QCL) array and digital-pixel focal plane array (DFPA) camera. Opt Express 2014, 22(12):14392-14401.
• [6]Kröger N, Egl A, Engel M, Gretz N, Haase K, Herpich I, Kränzlin B, Neudecker S, Pucci A, Schönhals A, Vogt J, Petrich W: Quantum cascade laser-based hyperspectral imaging of biological tissue. J Biomed Opt 2014, 19(11):111607.
• [7]Vaglio Laurin G, Cheung-Wai Chan J, Chen Q, Lindsell JA, Coomes DA, Guerriero L, Frate FD, Miglietta F, Valentini R: Biodiversity mapping in a tropical west african forest with airborne hyperspectral data. PLoS One 2014, 9(6):e97910.
• [8]Arslan H, Tasan M, Yildirim D, Koksal ES, Cemek B: Predicting field capacity, wilting point, and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches. Environ Monit Assess 2014, 186(8):5077-5088.
• [9]Duann JR, Jan CI, Ou-Yang M, Lin CY, Mo JF, Lin YJ, Tsai MH, Chiou JC: Separating spectral mixtures in hyperspectral image data using independent component analysis: validation with oral cancer tissue sections. J Biomed Opt 2013, 18(12):126005.
• [10]Noyel G, Angulo J, Jeulin D: Morphological segmentation of hyperspectral images. Image Anal Stereol 2007, 26(3):101-109.
• [11]Benediktsson JA, Palmason JA, Sveinsson JR: Classification of hyperspectral data from urban areas based on extended morphological profiles. Geoscience and Remote Sensing, IEEE Transactions on 2005, 43(3):480-491.
• [12]Plaza A, Martinez P, Plaza J, Perez R: Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. Geoscience and Remote Sensing, IEEE Transactions on 2005, 43(3):466-479.
• [13]Koprowski R, Teper S, Wrobel Z, Wylegala E: Automatic analysis of selected choroidal diseases in OCT images of the eye fundus. Biomed Eng Online 2013, 12:117. BioMed Central Full Text
• [14]Fauvel M, Benediktsson JA, Chanussot J, Sveinsson JR: Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. Geoscience and Remote Sensing, IEEE Transactions on 2008, 46(11):3804-3814.
• [15]Dalla MM, Villa A, Benediktsson JA, Chanussot J: Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. Geoscience and Remote Sensing Letters, IEEE 2011, 8(3):542-546.
• [16]Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J: Composite kernels for hyperspectral image classification. Geoscience and Remote Sensing Letters, IEEE 2006, 3(1):93-97.
• [17]Rellier G, Descombes X, Falzon F, Zerubia J: Texture feature analysis using a Gauss-Markov model in hyperspectral image classification. Geoscience and Remote Sensing, IEEE Transactions on 2004, 42(7):1543-1551.
• [18]Zhang X, Younan NH, O’hara CG: Wavelet domain statistical hyperspectral soil texture classification. Geoscience and Remote Sensing, IEEE Transactions on 2005, 43(3):615-618.
• [19]Ryu JH, Seo YK, Boo YC, Chang MY, Kwak TJ, Koh JS: A quantitative evaluation method of skin texture affected by skin ageing using replica images of the cheek. Int J Cosmet Sci 2014, 36(3):247-252.
• [20]Jablonski NG, Chaplin G: The evolution of skin pigmentation and hair texture in people of African ancestry. Dermatol Clin 2014, 32(2):113-121.
• [21]Bargo PR, Kollias N: Measurement of skin texture through polarization imaging. Br J Dermatol 2010, 162(4):724-731.
• [22]Pan Z, Healey G, Prasad M, Tromberg B: Face recognition in hyperspectral images. Pattern analysis and machine intelligence. IEEE Transactions on 2003, 25(12):1552-1560.
• [23]Stamatas GN, Balas CJ, Kollias N: Hyperspectral Image Acquisition and Analysis of Skin. Biomedical Optics 2003. International Society for Optics and Photonics 2003, 77-82.
• [24]Liu Z, Yan J, Zhang D, Li QL: Automated tongue segmentation in hyperspectral images for medicine. Appl Opt 2007, 46(34):8328-8334.
• [25]Tsumura N, Nakaguchi T, Ojima N, Takase K, Okaguchi S, Hori K, Miyake Y: Image-based control of skin melanin texture. Appl Opt 2006, 45(25):6626-6633.
• [26]Fiedler M, Meier WD, Hoppe U: Texture analysis of the surface of the human skin. Skin Pharmacol 1995, 8(5):252-265.
• [27]Stoecker WV, Chiang CS, Moss RH: Texture in skin images: comparison of three methods to determine smoothness. Comput Med Imaging Graph 1992, 16(3):179-190.
• [28]Li Q, Chen Z, He X, Wang Y, Liu H, Xu Q: Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology. Neurochem Int 2012, 61(8):1375-1384.
• [29]Zuzak KJ, Francis RP, Wehner EF, Litorja M, Cadeddu JA, Livingston EH: Active DLP hyperspectral illumination: a noninvasive, in vivo, system characterization visualizing tissue oxygenation at near video rates. Anal Chem 2011, 83(19):7424-7430.
• [30]Zuzak KJ, Gladwin MT, Cannon RO III, Levin IW: Imaging hemoglobin oxygen saturation in sickle cell disease patients using noninvasive visible reflectance hyperspectral techniques: effects of nitric oxide. Am J Physiol Heart Circ Physiol 2003, 285(3):H1183-H1189.
• [31]Dicker DT, Lerner J, Van Belle P, Barth SF, Guerry D, Herlyn M, Elder DE, El-Deiry WS: Differentiation of normal skin and melanoma using high resolution hyperspectral imaging. Cancer Biol Ther 2006, 5(8):1033-1038.
• [32]Samarov DV, Clarke ML, Lee JY, Allen DW, Litorja M, Hwang J: Algorithm validation using multicolor phantoms. Biomed Opt Express 2012, 3(6):1300-1311.
• [33]Rosas JG, Blanco M: A criterion for assessing homogeneity distribution in hyperspectral images. Part 1: homogeneity index bases and blending processes. J Pharm Biomed Anal 2012, 70:680-690.
• [34]Rosas JG, Blanco M: A criterion for assessing homogeneity distribution in hyperspectral images. Part 2: application of homogeneity indices to solid pharmaceutical dosage forms. J Pharm Biomed Anal 2012, 70:691-699.
• [35]Otsu N: A threshold selection method from gray-level histograms. IEEE Trans Sys, Man, Cyber 1979, 9(1):62-66.
• [36]Koprowski R, Wilczyński S, Wróbel Z, Kasperczyk S, Błońska-Fajfrowska B: Automatic method for the dermatological diagnosis of selected hand skin features in hyperspectral imaging. Biomed Eng Online 2014, 13:47. BioMed Central Full Text
• [37]Koprowski R: Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing. Biomed Eng Online 2014, 13:93. BioMed Central Full Text
• [38]Foster KR, Koprowski R, Skufca JD: Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014, 13:94. BioMed Central Full Text
• [39]Koprowski R, Teper S, Weglarz B, Wylęgała E, Krejca M, Wróbel Z: Fully automatic algorithm for the analysis of vessels in the angiographic image of the eye fundus. Biomed Eng Online 2012, 11:35. BioMed Central Full Text
• [40]Filik J, Rutter AV, Sulé-Suso J, Cinque G: Morphological analysis of vibrational hyperspectral imaging data. Analyst 2012, 137(24):5723-5729.
• [41]Olweny EO, Faddegon S, Best SL, Jackson N, Wehner EF, Tan YK, Zuzak KJ, Cadeddu JA: Renal oxygenation during robot-assisted laparoscopic partial nephrectomy: characterization using laparoscopic digital light processing hyperspectral imaging. J Endourol 2013, 27(3):265-269.
• [42]Edelman G, van Leeuwen TG, Aalders MC: Hyperspectral imaging for the age estimation of blood stains at the crime scene. Forensic Sci Int 2012, 223(1–3):72-77.
• [43]Akbari H, Halig LV, Schuster DM, Osunkoya A, Master V, Nieh PT, Chen GZ, Fei B: Hyperspectral imaging and quantitative analysis for prostate cancer detection. J Biomed Opt 2012, 17(7):076005.
• [44]Yudovsky D, Nouvong A, Schomacker K, Pilon L: Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry. J Biomed Opt 2011, 16(2):026009.
• [45]Akbari H, Uto K, Kosugi Y, Kojima K, Tanaka N: Cancer detection using infrared hyperspectral imaging. Cancer Sci 2011, 102(4):852-857.
• [46]Stam B, van Gemert MJ, van Leeuwen TG, Teeuw AH, van der Wal AC, Aalders MC: Can color inhomogeneity of bruises be used to establish their age? J Biophotonics 2011, 4(10):759-767.
• [47]Jaworek-Korjakowska J, Tadeusiewicz R: Assessment of dots and globules in dermoscopic color images as one of the 7-point check list criteria. IEEE International Conference on Image Processing, ICIP 2013, 6738299:1456-1460.
• [48]Jaworek-Korjakowska J, Tadeusiewicz R: Assessment of asymmetry in dermoscopic colour images of pigmented skin lesions. Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013, 2013:368-375.
• [49]Sonka M, Michael Fitzpatrick J: Medical Image Processing and Analysis. In Handbook of Medical Imaging. Belligham: SPIE; 2000.
• [50]Korzynska A, Hoppe A, Strojny W, Wertheim D: Investigation of a combined texture and contour method for segmentation of light microscopy cell images. In Proceedings of the Second IASTED International Conference on Biomedical Engineering. Anaheim, Calif. Calgary, Zurich: ACTA Press; 2004:234-239.
• [51]Korzynska A, Iwanowski M: Multistage morphological segmentation of bright-field and fluorescent microscopy images. Opt-Electron Rev 2012, 20(2):87-99.
• [52]Korus P, Dziech A: Efficient method for content reconstruction with self-embedding. IEEE Trans Image Process 2013, 22(3):1134-1147.
• [53]Tadeusiewicz R, Ogiela MR: Automatic understanding of medical images new achievements in syntactic analysis of selected medical images. Biocybern Biomed Eng 2002, 22(4):17-29.
• [54]Edelman GJ, Gaston E, van Leeuwen TG, Cullen PJ, Aalders MC: Hyperspectral imaging for non-contact analysis of forensic traces. Forensic Sci Int 2012, 223(1–3):28-39.
• [55]Bjorgan A, Milanic M, Randeberg LL: Estimation of skin optical parameters for real-time hyperspectral imaging applications. J Biomed Opt 2014, 19(6):66003.
• [56]Li Q, Sun Z, Wang Y, Liu H, Guo F, Zhu J: Histological skin morphology enhancement base on molecular hyperspectral imaging technology. Skin Res Technol 2013, 23:332-340.
• [57]Randeberg LL, Larsen EL, Svaasand LO: Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory. J Biophotonics 2010, 3(1–2):53-65.
• [58]Cancio LC, Batchinsky AI, Mansfield JR, Panasyuk S, Hetz K, Martini D, Jordan BS, Tracey B, Freeman JE: Hyperspectral imaging: a new approach to the diagnosis of hemorrhagic shock. J Trauma 2006, 60(5):1087-1095.
• [59]Zuzak KJ, Schaeberle MD, Lewis EN, Levin IW: Visible reflectance hyperspectral imaging: characterization of a noninvasive, in vivo system for determining tissue perfusion. Anal Chem 2002, 74(9):2021-2028.