Extraction of Target Fluorescence Signal from In Vivo Background Signal Using Image Subtraction Algorithm

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 Bai Department 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 signal is often contaminated by the high level of background signal originated from autofluorescence and leakage of excitation light. In this paper, we propose an image subtraction algorithm based on two images acquired using two excitation filters with different spectral regions. One in vivo experiment with a mouse locally injected with fluorescein isothiocyanate (FITC) was conducted to calculate the subtraction coefficient used in our studies and to validate the subtraction result when the exact position of the target fluorescence signal was known. Another in vivo experiment employing a nude mouse implanted with green fluorescent protein (GFP) expressing colon tumor was conducted to demonstrate the performance of the employed method to extract target fluorescence signal when the exact position of the target fluorescence signal was unknown. The subtraction results show that this image subtraction algorithm can 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 powerful modalities directed toward noninvasive, high-sensitive imaging 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, fluorescence reflectance imaging (FRI) is the most common method to record surface and subsurface fluorescence activity from entire animal, with combined simplicity of development and operation as well as high throughput [12,13]. Although FRI has gained wide applications in the field of fluorescence molecular imaging, a common issue encountered in practical applications is the background signal, which generally originates from autofluorescence of the animal (primarily from components in skin and food) especially in the visible spectrum [14], as well as the leakage of the excitation light due to imperfect fluorescent filters. In in vivo FRI experiments, the background signal may result in distorted or obscured image which significantly impairs the imaging fidelity. To overcome these limitations, various solutions have been proposed, such as employing narrow band-pass emission filters to isolate target fluorescence signal, using fluorescent probes which can be excited at wavelengths in the near-infrared (NIR) [3,15 17], and developing multi-spectral imaging techniques with the aid of special imaging systems and spectral unmixing algorithms to resolve target fluorescence signal [18 20]. However, in some practical cases, these approaches may be either infeasible or too complex. As an effective target signal extraction method, image Manuscript received July 30, 2010; revised March 9, 2012 This work was supported by National Basic Research Program of China (973 Programme) (No. 2011CB707701), National Major Scientific Instrument and Equipment Development Project (No. 2011YQ030114), National Natural Science Foundation of China (Nos. 81071191, 60831003, 30930092, and 30872633), Beijing Natural Science Foundation (No. 3111003), and Tsinghua-Yue-Yuen Medical Science Foundation. subtraction algorithm has been introduced in fluorescence imaging. However, in previously published studies, the subtraction coefficient (also called scale factor) used in the image subtraction process was determined empirically [21]. In this paper, we employed an image subtraction method with the subtraction coefficient determined by in vivo experiment to separate target fluorescence signal from background signal in FRI. In our studies, a charge-coupled device (CCD)-based fluorescence molecular imaging system has been employed to collect both fluorescence signal and background signal. A Kunming (KM) mouse injected with 20 μm fluorescein isothiocyanate (FITC) was employed to determine the subtraction coefficient in the image subtraction process. Then the image subtraction algorithm was performed to extract the target fluorescence signal originated from the locally injected FITC. Target-to-background ratio (TBR) was also analyzed to quantitatively demonstrate the performance of the used algorithm. Finally, the method was applied to detect colon tumor cells expressing green fluorescent protein (GFP) in a nude mouse. This paper is structured as follows. In Section 2, the methods used are detailed. In Section 3, in vivo experimental results are described. Finally, in Section 4, the major results of this study are concluded and future work is discussed. 2 Materials and methods 2.1 Experimental setup The system used for fluorescence signal and background signal acquisition is shown in Fig. 1, which is similar to that described in [22] except for the imaging experiments in this paper 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 performed by a 512 512 pixels, 70 C CCD camera (ii) (ixon DU- 897, Andor Technologies, Belfast, Northern Ireland) coupled with a 60 mm f/2.8 imaging lens (iii) (Nikon, Melville, NY, USA) placed on the opposite side of the imaged animal. A 545 ± 30 nm band-pass emission filter (iv) is employed in front of the CCD camera. A 250 W Halogen lamp (v) (7ILT250, 7-star, Beijing, PRC) equipped with a collimator lens (vi) which is used to generate a 10 cm 10 cm uniform excitation beam is mounted next to the camera, thus providing the ability to perform fluorescence imaging in the reflection geometry. Two band-pass excitation filters (vii) are used in front of the Halogen lamp for fluorescence image and background image collection, respectively. To be specific, for fluorescence signal measurements, a 465±22 nm band-pass excitation filter is used and for background signal measurements, a 425±15 nm band-pass excitation filter is used, while the emission signal is recorded with the same emission filter as discussed above. The central wavelength of the background excitation filter is chosen as 425 nm in order to be distinct from the peak excitation wavelength of the fluorophores. The imaged animal is anesthetized by an isoflurane veterinary vaporizer (VMR, Matrx, NY, USA) during in vivo imaging process. The total excitation light power delivered to the imaged animal is about 1 mw. the background excitation filter), as well as the sensitivity of the CCD camera for each wavelength used. k λex can be calculated from the relative intensities of the background signal over two spectral regions of the excitation filters λ ex and. In our studies, k λex is obtained by solving a least-squares problem: min S λex bg k λex S λbg 2 (2) where S λex bg represents the background signal in the fluorescence image S λex collected at the fluorophores excitation wavelength λ ex, which is exactly what we want to subtract from the total recorded signal to get the target fluorescence signal. After the determination of k λex, target fluorescence signal S tar f in the recorded fluorescence image S λex can be extracted according to (1). 2.3 Target-to-background ratio (TBR) In this paper, TBR is introduced to evaluate the performance of image subtraction algorithm. Typically, TBR reflects the signal intensity contrast within and outside of the region of interest (ROI). Here, ROI refers to the region where the target fluorophores are located. Target signal intensity is defined as the total pixel values within the ROI, T. Background signal intensity is defined as the total pixel values outside of the ROI, B. Thus, TBR can be calculated as follows: TBR = T B. (3) 3 Results The experimental setup of the fluorescence imaging sys- Fig. 1 tem 2.2 Image subtraction algorithm An image subtraction algorithm is employed in this paper to separate target fluorescence signal from background signal in in vivo imaging experiments. The subtraction procedure can be expressed as follows: S tar f = S λex k λex S λbg (1) where S λex denotes the fluorescence image (total recorded signal including both target fluorescence signal and background signal) collected at the fluorophores excitation wavelength λ ex, while S λbg denotes the background image collected at the background excitation wavelength. The subtraction coefficient k λex is a constant whose value mainly depends on the characteristics of the experimental setups, such as the central wavelength, the full width half maximum (FWHM) and the transmittance of the excitation filter pairs (including the fluorescence excitation filter and 3.1 Determination of subtraction coefficient To determine the coefficient for image subtraction algorithm, an 8-week-old KM mouse was employed, with hair removed from the lower part of its body. 20 μm FITCwas injected in the right leg of the mouse, and FRI was performed afterwards. Firstly, fluorescence image was acquired using the 465±22 nm band-pass excitation filter which could excite both target fluorescence signal as well as background signal. Then, the 425 ± 15 nm band-pass background excitation filter was used in place of the 465 ± 22 nm filter to collect background image which contains background signal only. The exposure time was set to 2 s and 4 4 CCD binning was used in this imaging experiment. Finally, the subtraction coefficient k λex can be calculated from the relative intensities of the background signal over the spectral regions of the two excitation filters. As the relative intensities of the background signals may vary slightly in different regions of the mouse torso, in our studies, 10 regions (each included 50 50 pixels) in the background region were randomly selected and corresponding subtraction coefficients were calculated according to (2), respectively. The final subtraction coefficient was determined by the mean of the 10 subtraction coefficients. Fig. 2 depicts 10 different regions to calculate subtraction coefficient, 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) Corresponding 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 λex Region 1 2 3 4 5 6 7 8 9 10 Mean Standard deviation k λex 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 corresponding regions in the background image. The green arrow in Fig. 2 (a) indicates the injection position of FITC. As shown in Table 1, the subtraction coefficients vary slightly from each other. The final subtraction coefficient was determined as k λex =3.71 according to the mean of the 10 subtraction coefficients. Subtraction coefficients between other excitation filter pairs can be obtained using similar method but are not referred to herein. 3.2 In vivo study 1 Since the subtraction coefficient k λex has been determined, image subtraction could be conducted based on (1) to extract target FITC fluorescence signal from background signal in the previously described KM mouse. Here, as FITC was locally injected, the injection region was considered as the ROI. Thus, target fluorescence signal T equals to the total pixel values within the ROI, and background signal B equals to the total pixel values outside of the ROI. TBRs before and after image subtraction were both calculated according to (3), in order to quantitatively analyze the ability of our method to extract target fluorescence signal and remove background signal. The white light image of the mouse with a green arrow indicating the injection position of FITC is depicted in Fig. 3 (a). Fig. 3 (b) shows the collected fluorescence image including both target fluorescence signal and background signal, and Fig. 3 (c) shows the background image collected at the background excitation wavelength. The red curve in Fig. 3 (b) indicates the ROI where FITC was injected. From Fig. 3 (b), we can find that the intensity of background signal outside of the ROI is relatively stronger in the original fluorescence image. After image subtraction, target fluorescence signal within the ROI is effectively enhanced, as shown in Fig. 3 (d). TBRs analysis results (see Table 2) further demonstrate the performance of the image subtraction algorithm quantitatively. The results suggest that the contrast between the target fluorescence signal and the background signal has been improved by 33.5 times after applying 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 effectively remove background signal and extract target fluorescence signal in obscured fluorescence image when the exact location of target fluorescence signal is unknown, we performed another in vivo study employing a nude mouse implanted 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-pass excitation filter which could excite both target fluorescence signal as well as background signal. Then, the 425 ± 15 nm band-pass background excitation filter was used in place of the 465 ± 22 nm filter to collect background image which contained background signal only. The exposure time was set to 5 s and 1 1 CCD binning was used in this experiment. Finally, image subtraction was conducted based on (1). As the same experimental setup was used in both in vivo studies, the same subtraction coefficient k λex =3.71 determined in Section 3.1 was employed here. The white light image of the GFP-expressing colon tumor mouse is depicted in Fig. 4 (a). Fig. 4 (b) shows the collected fluorescence image including both target fluorescence signal and background signal, and Fig. 4 (c) shows the background image collected at the background excitation wavelength. Since the exact position of the tumor is unknown, and the intensity of fluorescence signal is weak, it is difficult to distinguish the target tumor fluorescence just from the fluorescence image as shown in Fig. 4 (b). However, after applying image subtraction algorithm, the target tumor fluorescence signal in the lower abdomen is effectively extracted, as indicated by the green arrow in Fig. 4 (d). 4 Conclusions and future work Challenges remain in FRI of surface and subsurface fluorescence activity in small animals in vivo, since the target fluorescence signal is often contaminated by the high level of background signal originated from autofluorescence and leaked excitation light. This may significantly compromise the TBR and imaging fidelity of the FRI modality. Thus, an effective technique to separate target fluorescence signal from background signal is critical. In this paper, we mainly studied an image subtraction algorithm which is conducted based on two images collected by two excitation filters over different spectral regions. 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She is currently a Ph. D. candidate in the Department of Biomedical Engineering, Tsinghua University, Beijing, PRC. Her research interests include fluorescence molecular tomography for small animal imaging. E-mail: l-f08@mails.tsinghua.edu.cn Xin Liu received the bachelor and master degrees from the Fourth Military Medical University, Xi an, PRC in 2001 and 2006, respectively. He is now a Ph. D. candidate in the Department of Biomedical Engineering, Tsinghua University, Beijing, PRC. His research interests include fluorescence molecular tomography and medical image processing. E-mail: xin-liu08@mails.tsinghua.edu.cn Bin Zhang received the master degree in mechanical and electronic engineering from University of Science and Technology of China in 2007. From 2007 to 2009, he was a research assistant in Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, PRC. He is now a Ph. D. candidate in the Department of Biomedical Engineering, Tsinghua University, Beijing, PRC. His research interests include fluorescence molecular tomography for small animal. E-mail: zhbin2008@gmail.com Jing Bai received the M. Sc. and Ph. D. degrees from Drexel University, Philadelphia, PA, USA in 1983 and 1985, respectively. From 1985 to 1987, she was a research associate and assistant professor at the Biomedical Engineering and Science Institute, Drexel University. In 1988, 1991, and 2000, she became an associate professor, professor, and Cheung Kong chair professor at the Department of Biomedical Engineering, Tsinghua University, Beijing, PRC. Since 1997, she has been an associate editor for IEEE Transactions on Information Technology in Biomedicine. She has authored or coauthored ten books and more than 300 journal papers. Her research interests include mathematical modeling and simulation of cardiovascular system, optimization of cardiac assist devices, medical ultrasound, telemedicine, home health care network and home monitoring devices, and infrared imaging. E-mail: deabj@tsinghua.edu.cn (Corresponding author)