International Journal of Automation and Computing 9(3), June 2012, 232-236

DOI: 10.1007/s11633-012-0639-z

Extraction of Target Fluorescence Signal from In Vivo

Background Signal Using Image Subtraction Algorithm

Fei Liu Xin Liu Bin Zhang Jing BaiDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, PRC

Abstract: Challenges remain in fluorescence reflectance imaging (FRI) in in vivo experiments, since the target fluorescence signalis often contaminated by the high level of background signal originated from autofluorescence and leakage of excitation light. In thispaper, we propose an image subtraction algorithm based on two images acquired using two excitation filters with different spectralregions. One in vivo experiment with a mouse locally injected with fluorescein isothiocyanate (FITC) was conducted to calculate thesubtraction coefficient used in our studies and to validate the subtraction result when the exact position of the target fluorescencesignal was known. Another in vivo experiment employing a nude mouse implanted with green fluorescent protein (GFP) — expressingcolon tumor was conducted to demonstrate the performance of the employed method to extract target fluorescence signal when theexact position of the target fluorescence signal was unknown. The subtraction results show that this image subtraction algorithmcan effectively extract the target fluorescence signal and quantitative analysis results demonstrate that the target-to-background ratio(TBR) can be significantly improved by 33.5 times after background signal subtraction.

Keywords: Biomedical image processing, biomedical optical imaging, fluorescence, fluorescence reflectance imaging, imaging system.

1 Introduction

Optical imaging techniques are emerging as new power-ful modalities directed toward noninvasive, high-sensitiveimaging of disease pathogenesis[1−5] , drug development[6,7],and therapeutic response[8−10] in small animals in vivo[11].Among these optical imaging methodologies, fluorescencereflectance imaging (FRI) is the most common method torecord surface and subsurface fluorescence activity from en-tire animal, with combined simplicity of development andoperation as well as high throughput[12,13]. Although FRIhas gained wide applications in the field of fluorescencemolecular imaging, a common issue encountered in prac-tical applications is the background signal, which generallyoriginates from autofluorescence of the animal (primarilyfrom components in skin and food) especially in the vis-ible spectrum[14], as well as the leakage of the excitationlight due to imperfect fluorescent filters. In in vivo FRIexperiments, the background signal may result in distortedor obscured image which significantly impairs the imagingfidelity.

To overcome these limitations, various solutions havebeen proposed, such as employing narrow band-pass emis-sion filters to isolate target fluorescence signal, using fluo-rescent probes which can be excited at wavelengths in thenear-infrared (NIR)[3,15−17], and developing multi-spectralimaging techniques with the aid of special imaging systemsand spectral unmixing algorithms to resolve target fluores-cence signal[18−20] . However, in some practical cases, theseapproaches may be either infeasible or too complex.

As an effective target signal extraction method, image

Manuscript received July 30, 2010; revised March 9, 2012This work was supported by National Basic Research Program

of China (973 Programme) (No. 2011CB707701), National Ma-jor Scientific Instrument and Equipment Development Project(No. 2011YQ030114), National Natural Science Foundation of China(Nos. 81071191, 60831003, 30930092, and 30872633), Beijing NaturalScience Foundation (No. 3111003), and Tsinghua-Yue-Yuen MedicalScience Foundation.

subtraction algorithm has been introduced in fluorescenceimaging. However, in previously published studies, the sub-traction coefficient (also called scale factor) used in theimage subtraction process was determined empirically[21].In this paper, we employed an image subtraction methodwith the subtraction coefficient determined by in vivo ex-periment to separate target fluorescence signal from back-ground signal in FRI. In our studies, a charge-coupled de-vice (CCD)-based fluorescence molecular imaging systemhas been employed to collect both fluorescence signal andbackground signal. A Kunming (KM) mouse injected with20 μM fluorescein isothiocyanate (FITC) was employed todetermine the subtraction coefficient in the image subtrac-tion process. Then the image subtraction algorithm wasperformed to extract the target fluorescence signal origi-nated from the locally injected FITC. Target-to-backgroundratio (TBR) was also analyzed to quantitatively demon-strate the performance of the used algorithm. Finally, themethod was applied to detect colon tumor cells expressinggreen fluorescent protein (GFP) in a nude mouse.

This paper is structured as follows. In Section 2, themethods used are detailed. In Section 3, in vivo exper-imental results are described. Finally, in Section 4, themajor results of this study are concluded and future workis discussed.

2 Materials and methods

2.1 Experimental setup

The system used for fluorescence signal and backgroundsignal acquisition is shown in Fig. 1, which is similar to thatdescribed in [22] except for the imaging experiments in thispaper are performed in the reflectance imaging geometry.As shown in Fig. 1, the imaged animal is suspended onto

F. Liu et al. / Extraction of Target Fluorescence Signal from In Vivo Background Signal Using · · · 233

an x-y translation stage (i). Signal acquisition is performedby a 512 × 512 pixels, –70◦C CCD camera (ii) (iXon DU-897, Andor Technologies, Belfast, Northern Ireland) cou-pled with a 60 mm f/2.8 imaging lens (iii) (Nikon, Melville,NY, USA) placed on the opposite side of the imaged an-imal. A 545 ± 30 nm band-pass emission filter (iv) is em-ployed in front of the CCD camera. A 250 W Halogen lamp(v) (7ILT250, 7-star, Beijing, PRC) equipped with a colli-mator lens (vi) which is used to generate a 10 cm× 10 cmuniform excitation beam is mounted next to the camera,thus providing the ability to perform fluorescence imagingin the reflection geometry. Two band-pass excitation filters(vii) are used in front of the Halogen lamp for fluorescenceimage and background image collection, respectively. To bespecific, for fluorescence signal measurements, a 465±22 nmband-pass excitation filter is used and for background sig-nal measurements, a 425±15 nm band-pass excitation filteris used, while the emission signal is recorded with the sameemission filter as discussed above. The central wavelengthof the background excitation filter is chosen as 425 nm inorder to be distinct from the peak excitation wavelength ofthe fluorophores. The imaged animal is anesthetized by anisoflurane veterinary vaporizer (VMR, Matrx, NY, USA)during in vivo imaging process. The total excitation lightpower delivered to the imaged animal is about 1mW.

Fig. 1 The experimental setup of the fluorescence imaging sys-

tem

2.2 Image subtraction algorithm

An image subtraction algorithm is employed in this pa-per to separate target fluorescence signal from backgroundsignal in in vivo imaging experiments. The subtraction pro-cedure can be expressed as follows:

Star f = Sλex − k λexλbg

Sλbg(1)

where Sλex denotes the fluorescence image (total recordedsignal including both target fluorescence signal and back-ground signal) collected at the fluorophores′ excitationwavelength λex, while Sλbg

denotes the background im-age collected at the background excitation wavelength λbg .The subtraction coefficient k λex

λbg

is a constant whose value

mainly depends on the characteristics of the experimentalsetups, such as the central wavelength, the full width halfmaximum (FWHM) and the transmittance of the excitationfilter pairs (including the fluorescence excitation filter and

the background excitation filter), as well as the sensitivityof the CCD camera for each wavelength used.

k λexλbg

can be calculated from the relative intensities of the

background signal over two spectral regions of the excita-tion filters λex and λbg. In our studies, k λex

λbg

is obtained by

solving a least-squares problem:

min ‖Sλex bg− k λex

λbg

Sλbg‖2 (2)

where Sλex bgrepresents the background signal in the fluo-

rescence image Sλex collected at the fluorophores′ excitationwavelength λex, which is exactly what we want to subtractfrom the total recorded signal to get the target fluorescencesignal. After the determination of k λex

λbg

, target fluorescence

signal Star f in the recorded fluorescence image Sλex can beextracted according to (1).

2.3 Target-to-background ratio (TBR)

In this paper, TBR is introduced to evaluate the per-formance of image subtraction algorithm. Typically, TBRreflects the signal intensity contrast within and outside ofthe region of interest (ROI). Here, ROI refers to the regionwhere the target fluorophores are located. Target signal in-tensity is defined as the total pixel values within the ROI,T . Background signal intensity is defined as the total pixelvalues outside of the ROI, B. Thus, TBR can be calculatedas follows:

TBR =T

B. (3)

3 Results

3.1 Determination of subtraction coeffi-cient

To determine the coefficient for image subtraction algo-rithm, an 8-week-old KM mouse was employed, with hairremoved from the lower part of its body. 20 μM FITC wasinjected in the right leg of the mouse, and FRI was per-formed afterwards. Firstly, fluorescence image was acquiredusing the 465±22 nm band-pass excitation filter which couldexcite both target fluorescence signal as well as backgroundsignal. Then, the 425 ± 15 nm band-pass background exci-tation filter was used in place of the 465 ± 22 nm filter tocollect background image which contains background sig-nal only. The exposure time was set to 2 s and 4 × 4 CCDbinning was used in this imaging experiment. Finally, thesubtraction coefficient k λex

λbg

can be calculated from the rel-

ative intensities of the background signal over the spectralregions of the two excitation filters. As the relative intensi-ties of the background signals may vary slightly in differentregions of the mouse torso, in our studies, 10 regions (eachincluded 50×50 pixels) in the background region were ran-domly selected and corresponding subtraction coefficientswere calculated according to (2), respectively. The finalsubtraction coefficient was determined by the mean of the10 subtraction coefficients.

Fig. 2 depicts 10 different regions to calculate subtractioncoefficient, as outlined by the red rectangles in the images.The first row illustrates the 10 selected regions in the fluo-

234 International Journal of Automation and Computing 9(3), June 2012

Fig. 2 Different regions to calculate the subtraction coefficient. (a)–(j) Ten different regions in the fluorescence image; (k)–(t) Corre-

sponding regions in the background image (The red rectangles indicate the outline of the selected regions, and the green arrow in (a)

indicates the injection position of FITC)

Fig. 3 Image subtraction process. (a) White light image of the mouse (The green arrow indicates the injection position of FITC);

(b) Fluorescence image collected by the 465 ± 22 nm excitation filter; (c) Background image collected by the 425 ± 15 nm background

excitation filter; (d) Target fluorescence signal separated from background signal (The red curves in (b) and (d) outline the ROI where

FITC was injected)

Table 1 Calculation of subtraction coefficient k λexλbg

Region 1 2 3 4 5 6 7 8 9 10 Mean Standard deviation

k λexλbg

3.62 3.61 3.63 3.77 3.83 3.70 3.77 3.63 3.51 4.03 3.71 0.14

rescence image and the second row illustrates correspond-ing regions in the background image. The green arrow inFig. 2 (a) indicates the injection position of FITC. As shownin Table 1, the subtraction coefficients vary slightly fromeach other. The final subtraction coefficient was determinedas k λex

λbg

= 3.71 according to the mean of the 10 subtraction

coefficients. Subtraction coefficients between other excita-tion filter pairs can be obtained using similar method butare not referred to herein.

3.2 In vivo study 1

Since the subtraction coefficient k λexλbg

has been deter-

mined, image subtraction could be conducted based on (1)to extract target FITC fluorescence signal from backgroundsignal in the previously described KM mouse. Here, asFITC was locally injected, the injection region was consid-ered as the ROI. Thus, target fluorescence signal T equalsto the total pixel values within the ROI, and backgroundsignal B equals to the total pixel values outside of the ROI.TBRs before and after image subtraction were both calcu-lated according to (3), in order to quantitatively analyze theability of our method to extract target fluorescence signaland remove background signal.

The white light image of the mouse with a green ar-row indicating the injection position of FITC is depicted inFig. 3 (a). Fig. 3 (b) shows the collected fluorescence image

including both target fluorescence signal and backgroundsignal, and Fig. 3 (c) shows the background image collectedat the background excitation wavelength. The red curve inFig. 3 (b) indicates the ROI where FITC was injected. FromFig. 3 (b), we can find that the intensity of background sig-nal outside of the ROI is relatively stronger in the orig-inal fluorescence image. After image subtraction, targetfluorescence signal within the ROI is effectively enhanced,as shown in Fig. 3 (d). TBRs analysis results (see Table2) further demonstrate the performance of the image sub-traction algorithm quantitatively. The results suggest thatthe contrast between the target fluorescence signal and thebackground signal has been improved by 33.5 times afterapplying the image subtraction algorithm.

Table 2 TBRs before and after image subtraction

Before image subtraction After image subtraction

TBR 0.06 2.01

3.3 In vivo study 2

To further demonstrate the ability of our method to ef-fectively remove background signal and extract target flu-orescence signal in obscured fluorescence image when theexact location of target fluorescence signal is unknown, weperformed another in vivo study employing a nude mouseimplanted with GFP-expressing colon tumor. Firstly, fluor-

F. Liu et al. / Extraction of Target Fluorescence Signal from In Vivo Background Signal Using · · · 235

Fig. 4 Image subtraction for a nude mouse bearing GFP-expressing colon tumor: (a) White light image of the mouse; (b) Fluorescence

image collected by the 465 ± 22 nm excitation filter; (c) Background image collected by the 425 ± 15 nm background excitation filter;

(d) Target fluorescence signal separated from background signal (The green arrow indicates the target tumor fluorescence signal in the

lower abdomen)

escence image was acquired using the 465±22 nm band-passexcitation filter which could excite both target fluorescencesignal as well as background signal. Then, the 425 ± 15 nmband-pass background excitation filter was used in place ofthe 465 ± 22 nm filter to collect background image whichcontained background signal only. The exposure time wasset to 5 s and 1 × 1 CCD binning was used in this experi-ment. Finally, image subtraction was conducted based on(1). As the same experimental setup was used in both invivo studies, the same subtraction coefficient k λex

λbg

= 3.71

determined in Section 3.1 was employed here.The white light image of the GFP-expressing colon tu-

mor mouse is depicted in Fig. 4 (a). Fig. 4 (b) shows thecollected fluorescence image including both target fluores-cence signal and background signal, and Fig. 4 (c) showsthe background image collected at the background excita-tion wavelength. Since the exact position of the tumor isunknown, and the intensity of fluorescence signal is weak, itis difficult to distinguish the target tumor fluorescence justfrom the fluorescence image as shown in Fig. 4 (b). However,after applying image subtraction algorithm, the target tu-mor fluorescence signal in the lower abdomen is effectivelyextracted, as indicated by the green arrow in Fig. 4 (d).

4 Conclusions and future work

Challenges remain in FRI of surface and subsurface flu-orescence activity in small animals in vivo, since the targetfluorescence signal is often contaminated by the high levelof background signal originated from autofluorescence andleaked excitation light. This may significantly compromisethe TBR and imaging fidelity of the FRI modality. Thus,an effective technique to separate target fluorescence signalfrom background signal is critical. In this paper, we mainlystudied an image subtraction algorithm which is conductedbased on two images collected by two excitation filters overdifferent spectral regions. The in vivo imaging results showthat the proposed method can effectively extract the targetfluorescence signal shielded in high level of background sig-nal, and the contrast between the target fluorescence signaland background signal can be greatly improved after back-ground subtraction. The problem of background signal con-tamination also exists in the study of fluorescence moleculartomography (FMT)[23,24] . In the future, this feasible andeffective image subtraction algorithm could also be incor-porated into FMT studies for the reduction of backgroundnoise and improvement of reconstruction quality.

References

[1] R. A. Sheth, R. Upadhyay, L. Stangenberg, R. Sheth, R.Weissleder, U. Mahmood. Improved detection of ovariancancer metastases by intraoperative quantitative fluores-cence protease imaging in a pre-clinical model. GynecologicOncology, vol. 112, no. 3, pp. 616–622, 2009.

[2] P. Puvanakrishnan, J. Park, P. Diagaradjane, J. A.Schwartz, C. L. Coleman, K. L. Gill-Sharp, K. L. Sang, J. D.Payne, S. Krishnan, J. W. Tunnell. Near-infrared narrow-band imaging of gold/silica nanoshells in tumors. Journalof Biomedical Optics, vol. 14, no. 2, 024044, 2009.

[3] N. C. Deliolanis, J. Dunham, T. Wurdinger, J. L.Figueiredo, B. A. Tannous, V. Ntziachristos. In-vivo imag-ing of murine tumors using complete-angle projection fluo-rescence molecular tomography. Journal of Biomedical Op-tics, vol. 14, no. 3, 030509, 2009.

[4] J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre,V. Ntziachristos. Visualization of pulmonary inflammationusing noninvasive fluorescence molecular imaging. Journalof Applied Physiology, vol. 104, no. 3, pp. 795–802, 2008.

[5] E. L. Kaijzel, G. van der Pluijm, C. W. Lowik. Whole-bodyoptical imaging in animal models to assess cancer devel-opment and progression. Clinical Cancer Research, vol. 13,no. 12, pp. 3490–3497, 2007.

[6] K. Licha, C. Olbrich. Optical imaging in drug discovery anddiagnostic applications. Advanced Drug Delivery Reviews,vol. 57, no. 8, pp. 1087–1108, 2005.

[7] M. Rudin, R. Weissleder. Molecular imaging in drug dis-covery and development. Nature Reviews Drug Discovery,vol. 2, no. 2, pp. 123–131, 2003.

[8] X. Montet, J. L. Figueiredo, H. Alencar, V. Ntziachris-tos, U. Mahmood, R. Weissleder. Tomographic fluores-cence imaging of tumor vascular volume in mice. Radiology,vol. 242, no. 3, pp. 751–758, 2007.

[9] X. Montet, V. Ntziachristos, J. Grimm, R. Weissleder. To-mographic fluorescence mapping of tumor targets. CancerResearch, vol. 65, no. 14, pp. 6330–6336, 2005.

[10] V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D.Yessayan, E. Graves, A. J. Bogdanov, L. Josephson, R.Weissleder. Visualization of antitumor treatment by meansof fluorescence molecular tomography with an annexin v-cy5.5 conjugate. Proceedings of the National Academy ofSciences of the United States of America, vol. 101, no. 33,pp. 12294–12299, 2004.

[11] G. D. Luker, K. E. Luker. Optical imaging: Current appli-cations and future directions. Journal of Nuclear Medicine,vol. 49, no. 1, pp. 1–4, 2008.

[12] V. Ntziachristos. Fluorescence molecular imaging. AnnualReview of Biomedical Engineering, vol. 8, no. 1, pp. 1–33,2006.

[13] V. Ntziachristos, J. Ripoll, L. V. Wang, R. Weissleder.Looking and listening to light: The evolution of whole-bodyphotonic imaging. Nature Biotechnology, vol. 23, no. 3,pp. 313–320, 2005.

236 International Journal of Automation and Computing 9(3), June 2012

[14] A. Garofalakis, G. Zacharakis, H. Meyer, E. N. Economou,C. Mamalaki, J. Papamatheakis, D. Kioussis, V. Ntziachris-tos, J. Ripoll. Three-dimensional in vivo imaging of greenfluorescent protein-expressing T cells in mice with noncon-tact fluorescence molecular tomography. Molecular Imag-ing, vol. 6, no. 2, pp. 96–107, 2007.

[15] E. I. Altinoglu, T. J. Russin, J. M. Kaiser, B. M. Barth,P. C. Eklund, M. Kester, J. H. Adair. Near-infrared emit-ting fluorophore-doped calcium phosphate nanoparticles forin vivo imaging of human breast cancer. ACS Nano, vol. 2,no. 10, pp. 2075–2084, 2008.

[16] K. E. Adams, S. Ke, S. Kwon, F. Liang, Z. Fan, Y.Lu, K. Hirschi, M. E. Mawad, M. A. Barry, E. M.Sevick-Muraca. Comparison of visible and near-infraredwavelength-excitable fluorescent dyes for molecular imag-ing of cancer. Journal of Biomedical Optics, vol. 12, no. 2,024017, 2007.

[17] T. A. Zdobnova, S. G. Dorofeev, P. N. Tananaev, R.B. Vasiliev, T. G. Balandin, E. F. Edelweiss, O. A.Stremovskiy, I. V. Balalaeva, I. V. Turchin, E. N. Lebe-denko, V. P. Zlomanov, S. M. Deyev. Fluorescent immuno-labeling of cancer cells by quantum dots and antibodyscfv fragment. Journal of Biomedical Optics, vol. 14, no. 2,021004, 2009.

[18] J. R. Mansfield, K. W. Gossage, C. C. Hoyt, R. M. Lev-enson. Autofluorescence removal, multiplexing, and auto-mated analysis methods for in-vivo fluorescence imaging.Journal of Biomedical Optics, vol. 10, no. 4, 041207, 2005.

[19] H. Xu, B. W. Rice. In-vivo fluorescence imaging with amultivariate curve resolution spectral unmixing technique.Journal of Biomedical Optics, vol. 14, no. 6, 064011, 2009.

[20] Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P.L. Choyke, H. Kobayashi. Spectral fluorescence molecularimaging of lung metastases targeting HER2/neu. ClinicalCancer Research, vol. 13, no. 10, pp. 2936–2945, 2007.

[21] N. C. Deliolanis, T. Wurdinger, L. Pike, B. A. Tannous,X. O. Breakefield, R. Weissleder, V. Ntziachristos. In vivotomographic imaging of red-shifted fluorescent proteins.Biomedical Optics Express, vol. 2, no. 4, pp. 887–900, 2011.

[22] F. Liu, X. Liu, D. Wang, B. Zhang, J. Bai. A parallel exci-tation based fluorescence molecular tomography system forwhole-body simultaneous imaging of small animals. Annalsof Biomedical Engineering, vol. 38, no. 11, pp. 3440–3448,2010.

[23] M. Gao, G. Lewis, G. M. Turner, A. Soubret, V. Ntzi-achristos. Effects of background fluorescence in fluores-cence molecular tomography. Applied Optics, vol. 44, no. 26,pp. 5468–5474, 2005.

[24] S. Psycharakis, G. Zacharakis, A. Garofalakis, R. Favic-chio, J. Ripoll. Autofluorescence removal from fluorescencetomography data using multispectral imaging. In Proceed-ings of SPIE-OSA Biomedical Optics, Munich, Germany,vol. 6626, paper 6626 14, 2007.

Fei Liu received the bachelor degree inbiomedical engineering from Zhejiang Uni-versity, Zhejiang, PRC in 2008. She is cur-rently a Ph.D. candidate in the Depart-ment of Biomedical Engineering, TsinghuaUniversity, Beijing, PRC.

Her research interests include fluores-cence molecular tomography for small an-imal imaging.

E-mail: l-f08@mails.tsinghua.edu.cn

Xin Liu received the bachelor and mas-ter degrees from the Fourth Military Med-ical University, Xi′an, PRC in 2001 and2006, respectively. He is now a Ph. D. can-didate in the Department of BiomedicalEngineering, Tsinghua University, Beijing,PRC.

His research interests include fluores-cence molecular tomography and medicalimage processing.

E-mail: xin-liu08@mails.tsinghua.edu.cn

Bin Zhang received the master degreein mechanical and electronic engineeringfrom University of Science and Technologyof China in 2007. From 2007 to 2009, hewas a research assistant in Shenzhen Insti-tute of Advanced Integration Technology,Chinese Academy of Sciences, PRC. He isnow a Ph.D. candidate in the Departmentof Biomedical Engineering, Tsinghua Uni-versity, Beijing, PRC.

His research interests include fluorescence molecular tomogra-phy for small animal.

E-mail: zhbin2008@gmail.com

Jing Bai received the M. Sc. and Ph. D.degrees from Drexel University, Philadel-phia, PA, USA in 1983 and 1985, respec-tively. From 1985 to 1987, she was a re-search associate and assistant professor atthe Biomedical Engineering and Science In-stitute, Drexel University. In 1988, 1991,and 2000, she became an associate profes-sor, professor, and Cheung Kong chair pro-fessor at the Department of Biomedical En-

gineering, Tsinghua University, Beijing, PRC. Since 1997, she hasbeen an associate editor for IEEE Transactions on InformationTechnology in Biomedicine. She has authored or coauthored tenbooks and more than 300 journal papers.

Her research interests include mathematical modeling andsimulation of cardiovascular system, optimization of cardiac as-sist devices, medical ultrasound, telemedicine, home health carenetwork and home monitoring devices, and infrared imaging.

E-mail: deabj@tsinghua.edu.cn (Corresponding author)