WHITE-LIGHT spectroscopy has the potential to be an

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1 IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 1 Narrow-Band Frequency Analysis for White-Light Spectroscopy Diagnostics Kaloyan A. Popov and Timothy P. Kurzweg, Senior Member, IEEE Abstract Precancerous conditions in tissue are often characterized by a slight increase in the nuclei size of epithelium cells. There has been research in the determination of precancerous tissue using white-light spectroscopy as an optical biopsy. In this paper, we investigate white light scattering off of tissue phantoms, created with polystyrene microspheres. When analyzing scattered white light, it is well known that the size of the scatterer contributes to a specific spatial oscillation pattern as a function of the wavelength. However, when examining a mixture of two or more different sized scatterers, it is difficult to relate this oscillation pattern to the specific scatterer sizes composing the mixture. To overcome this challenge, we convert this spatial oscillation pattern into the Fourier domain, which emphasizes a signature frequency peak for each particular component of the mixture. To improve our results, we use a narrow bandpass optical filter when interrogating the sample. This reduces noise in the frequency domain and isolates a single signature frequency for each scatterer in the mixture. Index Terms Biosensing, Fourier domain analysis, frequency components, tissue phantoms, white light scattering. I. INTRODUCTION WHITE-LIGHT spectroscopy has the potential to be an effective technique for determining precancerous conditions in tissue. Precancerous conditions are often characterized by a slight increase in the nuclei size of epithelium cells [1]. Epithelial nuclei can be studied as efficient optical scatterers as their refractive index is greater than those of the surrounding medium. Typical refractive index values are of the order of for nuclei, and the index of refraction of the surrounding cytoplasm is of the order of [1]. It is the morphology, in particular, the size, of the scattering element that the white-light spectroscopy can determine. The technique is based on a minimally invasive optical fiber probe used to both illuminate and collect backscattered light from the tissue. The relationship between spherical scatterers and their scattering behavior in white light is well described by Mie and Rayleigh Debye Gans (RDG) theory [2], [3]. The spatial optical spectrum, in terms of scattered intensity versus wavelength, of such particles exhibits specific oscillation patterns, relating the number of oscillations to the morphology of the scatterer [4] [8]. Scattering from spherical particles is a classical model for calculating the intensity and Manuscript received July 28, 2009; revised October 8, This work was supported in part by the National Science Foundation under Grant #ECCS The authors are with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA USA ( kap73@drexel.edu; kurzweg@ece.drexel.edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /JSTQE behavior of the scattered light according to Lorenz Mie theory. Most of the investigations have used polystyrene microspheres as a model of tissue phantoms for preliminary experimental studies of light scattering from cells [4], [6], [7], [9], [10]. Various researchers have shown that the unpolarized scattering spectra off of phantom tissues created with polystyrene microspheres have an oscillatory pattern, unique for different size of microspheres [4], [6], [8], [9]. The oscillation pattern of Mie spectra is described by interference among all spectral components between the incident light and the scattered light. In the case of tissue phantoms illuminated with a white light source in the visible range of nm, there is negligible absorption, and the interference is due to the interaction between incident and scattered spectral components. This is described through wave optics approximations, via Fresnel laws, according to the general theory for Mie scatterers, which are defined as scatterer sizes greater than the incident wavelength [2], [3]. Van de Hulst et al. [3] explains the oscillation pattern of the Mie spectra, giving detailed mathematical background in terms of scattering amplitude functions, which are well defined from Lorenz Mie theory. In our previous work [11], we provide a theoretical model with detailed equations for describing the spectra from biological cells. It is based on RDG scattering, which is in a good agreement with scatterer diameters in the range (d = 1 15 µm) used in our experimental measurements. The physical basis of RDG scattering is straightforward. Every volume element undergoes Rayleigh scattering independently of the other volume elements. The waves scattered in different directions from all these volumes interfere due to the different locations of volume elements in space. In order to calculate the interference effects, we refer to the phases of all scattered waves to a common origin of coordinates. Using superposition to add the complex amplitudes, which implies introducing a phase factor, describes the phase lag between the incident and scattered waves [3]. White-light spectroscopy has been proposed for many years; however, it has yet to be widely accepted for precancerous determination. The linear relationship between the number of spectral oscillations in the scattered light and the size of the scatterer is well known [4] [8]. However, when analyzing these spatial oscillations of a mixture of different sized cells, it is difficult to determine the relationship of the individual scatterers. The research presented in this paper overcomes many of these challenges when examining mixtures of scatterers, while also simplifying the size classification of individual scatterers, by limiting the optical spectrum and converting the spatial oscillations into the frequency domain using the Fourier transform. Other researchers have studied transforming the spatial X/$ IEEE

2 2 IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS Fig. 1. Spatial white light spectrum of 6 µm spheres. Fig. 3. Spatial white light spectrum of 15 µm spheres. Fig. 2. Spatial white light spectrum of 10 µm spheres. Fig. 4. Spatial white light spectrum of a mixture or 6 and 10 µm spheres. oscillations into the frequency domain [12], [13]. These researchers fit the frequency results to a second-order polynomial for classification of the scatterer size. Our research differs in that we do not use any polynomial fitting for scatterer size classification. We achieve good classification by using an optical filter to limit the wavelengths of interrogation on the sample. By reducing this optical input spectrum, we show that a single signature frequency can be determined for any sized scatterer. We experimentally prove a linear relationship between the signature frequency and the size of the scatterer. Using our classification technique in the frequency domain, we also show that we can determine the individual scatterers that compose a mixture of scatterers. II. MOTIVATION To motivate our research, we first present some of the classical spatial oscillations from phantom tissues, constructed from polystyrene microspheres. Spatial scattered data from phantom tissues composed of 6, 10, and 15 µm are shown in Figs. 1 3, respectively. The experimental setup used to gather these results will be detailed in the next section. As expected, the number of oscillations increases for the larger size scatterers. However, examining these oscillatory patterns, you can imagine the challenge of counting the peaks to determine the scatterer size. The peaks are not clearly defined, and also are not uniform in their frequency. Further complicating white-light spectroscopy analysis, mixtures of different sized scatterers, typical in real tissues, cause many nonuniform peaks. Fig. 4 shows the spatial response of light scattered from a phantom tissue created by a mixture of 6 and 10 µm polystyrene microspheres. In this case, the oscillations alone cannot lead to a determination of the scatterer size, as the resulting spectra is a superposition of both sized scatterers. There is a need for the further analysis of these data to accurately predict the size of the scatterers, both when in a homogeneous and heterogeneous tissue. By examining these data in the frequency domain, as well as limiting the input optical spectrum, we are able to determine signature frequencies related to the size of the scatterer. III. EXPERIMENTAL SETUP The experimental setup is illustrated in Fig. 5. The system is composed of a white light source (Dolan Jenner Industries,

3 POPOV AND KURZWEG: NARROW-BAND FREQUENCY ANALYSIS FOR WHITE-LIGHT SPECTROSCOPY DIAGNOSTICS 3 Fig. 5. Experimental setup for white-light spectroscopy measurements. Fiber-Lite PL-900) and a USB2000 optical spectrometer (Ocean Optics, Inc.) with a wavelength resolution of 0.3 nm and pixel charge-coupled device (CCD) array detector. We use a 200-µm bifurcated optical fiber probe (Ocean Optics, Inc.) in a backscattering collection configuration. The tip of the probe has a 30 angled window, which eliminates specular reflectance from the sample surface. The probe consists of one central fiber and a ring of six surrounding 200 µm core fibers, each with a numerical aperture of The central fiber is used for light delivery, and the surrounding ring is used to collect the scattered light. A filter holder is attached to the fiber holder such that the filtered light enters directly into the delivery fiber. We use 6, 10, and 15 µm polystyrene microspheres (Polysciences, Inc.) as a scattering media that acts as our phantom tissue. These sizes are chosen to represent healthy and cancerous tissue, as the size of healthy epithelial nuclei is of the order of 4 7 µm and cancerous nuclei can be up to 20 µm [1]. The refractive index of the spheres in the visible range is n = 1.5 [8], compared to the approximate value of tissue cells, n = 1.47 [1]. Throughout this paper, all microsphere samples will be in 1 ml deionized water suspensions, housed in a cuvette. All volumes of polystyrene microspheres in a homogeneous sample have a concentration of approximately particles/ml. When a heterogeneous mixed sized phantom tissue sample is created, each sized microsphere has an individual concentration of approximately particles/ml. We collect a background reference spectrum for the input light, obtained from light scattered off of a white light standard from Ocean Optics (WS-1). After backscattered data are collected from a sample, we subtract this light background spectrum from the raw spatial data. This result is then smoothed using a 6-point average fast Fourier transform (FFT) smoothing filter, which reduces the high-frequency noise of the backscattering data. For all data samples, the spectrometer has a 100-ms integration time. IV. EXPERIMENTAL RESULTS A. Initial Fourier Results of Full Spectrum The system described in the previous section is first used to interrogate homogeneous phantom tissue samples composed of microspheres with diameters of 6, 10, and 15 µm. The spatial results of these samples were previously shown in Figs Fig. 6. Fourier transform of the white light spectrum of a phantom tissue composed of 6 µm spheres. Fig. 7. Fourier transform of the white light spectrum of a phantom tissue composed of 10 µm spheres. Converting these spatial domain data using a discrete FFT results in the frequency domain diagrams in Figs. 6 8, shown in Fourier amplitude versus wavenumber. In the frequency responses in Figs. 6 8, the signal is noisy, and a single characteristic signature frequency is not obvious for each of the scatterers. However, it can be seen in the figures that the range of frequencies does shift to higher frequencies as the size of the scatterer increases. Fig. 9 shows the Fourier transform of a spatial domain sample of a mixture of 6 and 10 µm microspheres. Giving us preliminary confidence in our technique, it can be seen that the frequency spectrum of the mixture appears to be a superposition of the individual homogeneous samples observed in Figs. 6 and 7. With these promising initial results, we expect to find a clear, preferably singular, frequency that represents any size of scatterer. To achieve this, we look at a limited portion of the optical spatial spectrum.

4 4 IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS Fig. 8. Fourier transform of the white light spectrum of a phantom tissue composed of 15 µm spheres. Fig. 9. Fourier transform of the white light spectrum of a phantom tissue composed of 6 and 10 µm spheres. B. Optical Filter To reduce the number of frequencies representing each individual scatterer size along with the noise in the Fourier domain, we reduce the input optical spectrum to a limited range. The motivation behind the limited spectrum is that, in a smaller spatial domain, the oscillations will be more uniform; however, they will still contain significant information about the morphology of the scatterers. Therefore, we place different bandpass optical filters into the optical path before the light interrogates the sample, as shown in the experimental setup in Fig. 5. Choosing a frequency range in the middle of the visible spectrum, we examined the spectroscopy results using three different filters, each centered at 550 nm. The full-width at half-maximum (FWHM) of the filters that we place into the systems are 80 ± 25 nm (Edmund Optics, Inc.), 40 ± 8 nm (Andover Corporation), and 20 ± 4 nm (Andover Corporation). The experimental measured FWHM of these filters was 100, 44, and 21 nm, respectively. To determine which bandpass filter works best in the formation of a dominate signature frequency relating to a specificsized scatterer, we characterize all the homogeneous phantom tissue samples with the three optical filters. For brevity, we only present the results in the Fourier domain for the 6 µm samples, which can be seen for each of the filters in Fig. 10(a) (c). With the 80 nm filter [see Fig. 10(a)], the frequency content is reduced in comparison with the full white light spectrum without a filter, shown previously in Fig. 6. However, there are still multiple characteristic peaks for the scatterer size; therefore, we try a smaller filter. The frequency domain using the 40 nm filter [see Fig. 10(b)] shows a distinguished and clear peak at approximately ± cm 1, representing the signature frequency for spheres with 6 µm of diameter. For completeness, we next use a 20-nm FWHM filter [see Fig. 10(c)], which produces a similar result to the 40 nm filter, however, not quite as a distinct peak. After examining other different sized microsphere samples, we determined to use the 40 nm filter in our experimental measurements. Fig. 11(a) and (b) shows the signature frequency when using the 40 nm filter for 10 and 15 µm spheres in phantom tissues. For 10 µm spheres, the signature frequency is approximately ± cm 1, and for the 15 µm spheres, the signature frequency is approximately ± cm 1. We experimentally calculate the tolerance for these frequency measurements by gathering ten results for each sample. The signature frequency values given in the paper are the average results from these measurements. We find that the peak frequencies fell within ± cm 1 of this average value. Plotting these results, as seen in Fig. 11(c), we find a linear relationship between the size of the scatterers and the signature frequency [11] [13]. Using this linear relation, we can predict what the signature frequency would be for scatterer sizes that we have not experimentally characterized. Since the signature frequencies have a tolerance of ± cm 1, the corresponding size classification has a tolerance of ±0.335 µm. In these figures, the frequency peak at the beginning of the frequency spectra (approximately cm 1 ) is due to the dc component coming from the spectral shape of the input light source and the filter used in the experimental setup. The corresponding dc frequency is the average value of the input data sequence determined by the Fourier integration [14]. To reduce the magnitude of this dc frequency, the source spectra (light source through the filter) are subtracted from the scattered data obtained with the spectrometer. However, complete elimination of this dc value is difficult. C. Mixture Analysis Our primary goal in this paper is to analyze heterogeneous phantom tissue mixtures of microspheres. We have shown that by using the spatial domain alone, we cannot accurately determine the size of the scatterers that compose the mixed sample. Therefore, we use the Fourier domain, with samples interrogated with a 40-nm optical filter, to increase our ability to determine the composition of the mixture. We first make a phantom tissue mixture sample containing both 6 and 10 µm spheres, each with an equal concentration in a 1-mL cuvette. The spatial domain is shown in Fig. 12(a). From the spatial domain alone, the oscillations do not define the composition of the sample. However, when examining the results in

5 POPOV AND KURZWEG: NARROW-BAND FREQUENCY ANALYSIS FOR WHITE-LIGHT SPECTROSCOPY DIAGNOSTICS 5 Fig. 10. Fourier transform of white light spectrum of 6 µm spheres using optical spectrum limited by filters with sizes of (a) 80 nm, (b) 40 nm, and (c) 20 nm. Fig. 11. Frequency spectrum with the 40 nm optical filter for samples composed of (a) 10 µm and(b)15µm spheres. (c) Linear relationship between signature frequency and the scatterer size. Fig. 12. (a) Spatial white light spectrum and (b) frequency spectrum (with 40 nm optical filter) from a mixture of 6 and 10 µm spheres. the frequency domain, Fig. 12(b), two major signature frequency peaks appear, at approximately and cm 1. These two peaks relate to the two different sized components in the sample. The value cm 1 relates to the 6 µm spheres, and the value cm 1 is the signature for the 10 µm spheres. Therefore, we have been able to accurately determine the composition of the two sized mixture. For further testing of our technique, we create a triple phantom tissue mixture composed with 6, 10, and 15 µm spheres, each again with an equal concentration in a 1-mL cuvette. In Fig. 13(a), the spatial domain is shown, again emphasizing the difficulty, determining what sized spheres are used within the mixture. However, when examining the Fourier domain of the sample, as seen in Fig. 13(b), signature peaks arise, and in this case, at approximately , , and cm 1. These three frequencies correspond to the 6, 10, and 15 µm spheres, respectively; however, with a slight frequency tolerance due to noise or nonuniformity in the sample. In Figs. 12 and 13, the amplitude of the frequency peak of the 6 µm sample is greater than the 10 and 15 µm samples. The increased scattering intensity can be explained by the Tyndall effect [15], which states that particles in aggregates or clusters

6 6 IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS Fig. 13. (a) Spatial white light spectrum and (b) frequency spectrum (with 40 nm optical filter) from a mixture of 6, 10, and 15 µm spheres. that occupy the same volume as single particles will scatterer more. Depending on the radius of the cluster and the number of particles in the aggregate, the scattering intensity can be higher than those of the same particles when not in aggregate formation. The Tyndall effect also explains when clusters are formed by particles of different sizes. Clusters formed with smaller particles have a larger overall cluster radius than those clusters formed by larger particles [16], [17]. Therefore, clusters formed by smaller particles scatter more light. Cluster formation and properties are also valid for biological cells [18]. Our experimental results support this theory, as the 6 µm spheres scatter more than the 10 and 15 µm cases, and due to the same effect, the 10 µm samples scatter more than the 15 µm spheres. VI. CONCLUSION Frequency-domain analysis, along with limiting the optical spectrum with a bandpass filter, has proved to be an effective way to determine the scatterer size in phantom tissues using white-light spectroscopy. In addition, the technique allows the determination of the composition of heterogeneous mixtures containing different sized scatterers. We are currently researching how the amplitude of the signature frequency peaks relate to the concentration of the scatterers, especially in mixed samples, as well as applying our technique to real biological tissue. With the promise demonstrated in this paper, optical white-light spectroscopy might finally be used for precancerous detection and monitoring through a true optical biopsy. V. DISCUSSION When examining the spatial spectra obtained with the 40 nm filter [see Fig. 12(a) and 13(a)], the graphs appear almost identical. There are small spatial oscillations, or ripples, on the top of the intensity curve, making it almost impossible to say what sized scatters are being used, or if the sample contains a mixture. When converting into the frequency domain, signature frequency peaks appear, allowing the determination of the size(s) that compose the sample. We note the difference between using an optical filter instead of selecting a limited wavelength range from the entire spectrum. We use the optical filter to greatly ease our analysis and retain good resolution in our results. We collect data from the complete wavelength region allowable by the spectrometer (approximately nm), even with the filter in place. The Fourier transform is performed on all of these data points. By using the optical filter, a direct transform into the frequency domain, after removal of the light background signal, is possible. If only a selected wavelength region would be used from an entire dataset obtained without a filter, the signal would have to be manipulated to ensure that the data signal at the edges of the spectral ranges is practically zero to prevent aliasing. In addition, the resulting resolution in the frequency domain remains at the highest level when all the available data points are used. REFERENCES [1] V. Tuchin, Light Scattering Methods and Instruments for Medical Diagnosis, 2nd ed. Bellingham, WA: SPIE, [2] C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles. New York: Wiley, [3] H. C. Van de Hulst, Light Scattering by Small Particles. New York: Dover, [4] A. Amelink, M. P. L. Bard, S. A. Burgers, and H. J. C. M. Sterenborg, Single-scattering spectroscopy for the endoscopic analysis of particle size in superficial layers of turbid media, Appl. Opt., vol. 42, no. 19, pp , [5] V. Backman, R. Gurjar, K. Badizadegan, I. Itzkan, R. R. Dasari, L. T. Perelman, and M. S. Feld, Polarized light scattering spectroscopy for quantitative measurement of epithelial cellular structures in situ, IEEE J. Sel. Topics Quantum Electron., vol. 5, no. 4, pp , Jul./Aug [6] M. Canpolat and J. R. Mourant, Particle size analysis of turbid media with a single optical fiber in contact with the medium to deliver and detect white light, Appl. Opt., vol. 40, no. 22, pp , [7] L. T. Perelman, V. Backman, M. Wallace, G. Zonios, R. Manoharan, A. Nusrat, S. Shields, M. Seiler, C. Lima, T. Hamano, I. Itzkan, J. V. Dam, J. M. Crawford, and M. S. Feld, Observation of periodic fine structure in reflectance from biological tissue: A new technique for measuring nuclear size distribution, Phys. Rev. Lett., vol. 80, pp , [8] E. A. Vitol, T. P. Kurzweg, and B. Nabet, Using white-light spectroscopy for size determination of tissue phantoms, in Proc. SPIE, Conf. Prot. North, 2005, pp H H.10. [9] J. R. Mourant, T. Fuselier, J. Boyer, T. M. Johnson, and I. J. Bigio, Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms, Appl. Opt., vol.36,no. 4,pp , 1997.

7 POPOV AND KURZWEG: NARROW-BAND FREQUENCY ANALYSIS FOR WHITE-LIGHT SPECTROSCOPY DIAGNOSTICS 7 [10] J. R. Mourant, T. M. Johnson, S. Carpenter, A. G. T. Aida, and J. P. Freyer, Polarized angular dependent spectroscopy of epithelial cells and epithelial cell nuclei to determine the size scale of scattering structures, J. Biomed. Opt., vol. 7, no. 3, pp , [11] K. A. Popov and T. P. Kurzweg, Light scattering by ellipsoidal particles and Fourier analysis in the frequency domain, Proc. SPIE, vol. 7187, pp , [12] L. B. Scaffardi, F. A. Videla, and D. C. Schinca, Visible and near-infrared backscattering spectroscopy for sizing spherical microparticles, Appl. Opt., vol. 46, no. 1, pp , [13] F. A. Videla, D. Schinca, and L. B. Scaffardi, Sizing particles by backscattering spectroscopy and Fourier analysis, Opt. Eng., vol. 45, no. 4, pp , [14] W. L. Briggs and V. E. Henson, The DFT An Owner s Manual for the Discreet Fourier Transform. Philadelphia, PA: Society for Industrial Mathematics, [15] G. M. Wang and C. M. Sorensen, Diffusive mobility of fractal aggregates over the entire Knudsen number range, Phys. Rev., vol. E-60, pp , [16] M. M. Takayasu and F. Galembeck, Determination of the equivalent radii and fractal dimension of polystyrene latex aggregates from sedimentation coefficients, J. Colloid Interface Sci., vol. 202, no. 1, pp , Jun [17] A. V. Filippov, M. Zurita, and D. E. Rosner, Fractal-like aggregates: Relation between orphology and physical properties, J. Colloid Interface Sci., vol. 229, no. 1, pp , Sep [18] V. Tsai and N. J. Zvaifler, Dendritic cell-lymphocyte clusters that form spontaneously in rheumatoid arthritis synovial effusions differ from clusters formed in human mixed leukocyte reactions, J. Clin. Invest.,vol.82, pp , Nov Kaloyan A. Popov received the B.S. degree in quantum electronics and laser techniques and the M.S. degree in laser physics and optics from Sofia University Sveti Kliment Ohridski, Sofia, Bulgaria, in 2002 and 2004, respectively. He is currently working toward the Ph.D. degree with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA. During , he was with Bulgarian Academy of Sciences, where he developed multiwavelength tunable pulsed laser system with independent tuning of each wavelength for applications in differential absorption spectroscopy and light detection and ranging systems. His current research interests include precancerous detection using white-light spectroscopy. Mr. Popov is a member of the International Society for Optical Engineers. Timothy P. Kurzweg (S 92 M 95 SM 09) received the B.S. degree from Pennsylvania State University, University Park, in 1994, and the M.S. and Ph.D. degrees from the University of Pittsburgh, Pittsburgh, PA, in 1997 and 2002, respectively, all in electrical engineering. During 1999, he was with Microcosm (now Coventor), Cambridge, MA, where he developed an optical methodology to interface with a system-level analysis tool enabling optical microelectromechanical simulation (MEMS). He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA. His current research interests include programmable imaging with optical MEMS, precancerous detection using white-light spectroscopy, diffuse optical communication using space-time coding, and transparent conductive polymer antennas. Dr. Kurzweg is a member of the IEEE Lasers and Electro-Optics Society, the International Society for Optical Engineers, and the Optical Society of America.