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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 10 — Oct. 1, 2011
  • pp: 2821–2836
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Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

P. Beatriz Garcia-Allende, Iakovos Amygdalos, Hiruni Dhanapala, Robert D. Goldin, George B. Hanna, and Daniel S. Elson  »View Author Affiliations


Biomedical Optics Express, Vol. 2, Issue 10, pp. 2821-2836 (2011)
http://dx.doi.org/10.1364/BOE.2.002821


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Abstract

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.

© 2011 OSA

1. Introduction

Computer analysis of ophthalmologic pathologies using OCT, such as segmentation and quantification, traditionally relies on the extraction of thickness and size measures from the OCT images. However, for imaging in the gastrointestinal tract, such defined layers are usually not observed. In this regard, texture analysis of OCT images has shown promising results [3

3. X. Qi, M.V. Sivak Jr., G. Isenberg, J.E. Willis, and A.M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006). [CrossRef] [PubMed]

, 12

12. K.W. Gossage, J.J. Rodriguez, and J.K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003). [CrossRef] [PubMed]

15

15. X. Qi, Y. Pan, S.V. Sivak Jr., J.E. Willis, G. Isenberg, and A.M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomedical Optics Express 1, 825–847 (2010). [CrossRef]

]. These approaches are based on the assumption that the loss of structure associated with normal histological organization presumably resulting from the altered tissue architecture of the dysplastic tissue can be quantified as texture features, such as smoothness, coarseness and homogeneity, etc. in OCT images [15

15. X. Qi, Y. Pan, S.V. Sivak Jr., J.E. Willis, G. Isenberg, and A.M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomedical Optics Express 1, 825–847 (2010). [CrossRef]

]. Therefore, these features could be employed for tissue classification at a later stage. Among these alternatives, feature extraction derived from the two-dimensional discrete Fourier transform (DFT) has proved to be a feasible option [13

13. K.W. Gossage, C.M. Smith, E.M. Kanter, L.P. Hariri, A.L. Stone, J.J. Rodriguez, S.K. Williams, and J.K. Barton, “Texture analysis of speckle in optical coherence tomography images of tissue phantoms,” Phys. Med. Biol. 51, 1563–1575 (2006). [CrossRef] [PubMed]

], since DFT features can detect texture periodicity and orientation. A region of interest (ROI) is selected from the OCT images and the complex two-dimensional Fourier transform of the region is obtained. The latter is divided into four concentric square rings based on frequency (with the outermost ring representing the highest spatial frequency content) and then the magnitudes of the spatial frequencies in each ring are integrated and normalized to the total signal magnitude, such that each feature value represents the percentage of signal within a certain range of spatial frequencies. Images that contain large relatively homogenous areas, such as the epithelial region in normal oesophagus, would have high values for DFT features associated with lower spatial rings while images with lots of smaller inhomogeneous areas as the crypt-like glandular structures in Barrett’s oesophagus, would have higher values in the DFT features that correspond to higher spatial frequency rings [13

13. K.W. Gossage, C.M. Smith, E.M. Kanter, L.P. Hariri, A.L. Stone, J.J. Rodriguez, S.K. Williams, and J.K. Barton, “Texture analysis of speckle in optical coherence tomography images of tissue phantoms,” Phys. Med. Biol. 51, 1563–1575 (2006). [CrossRef] [PubMed]

, 14

14. Y. Chen, A.D. Aguirre, P.L. Hsiung, S.W. Huang, H. Mashimo, J.M. Schmitt, and J.G. Fujimoto, “Effects of axial resolution improvement on optical coherence tomography (OCT) imaging of gastrointestinal tissues,” Opt. Express 16, 2469–2485 (2008). [CrossRef] [PubMed]

].

One of the key issues is that healthcare processes and decision making will be favored by near real time computer-aided diagnosis (CAD) and, consequently, texture analysis procedures that simultaneously perform feature quantification and data compression (each tomogram is in the end represented by a short series of numbers depending on the particular textural approach employed) are preferable to a straightforward PCA analysis of the depth intensity profiles because the latter has a much higher computational load. However, both the distinct textural analysis techniques previously proposed and also the first attempt in a statistical study of the intensity distribution are subject to the selection of an appropriate ROI for the quantification of the image features or the disposal of physiological information for region segmentation. A two-step methodology is reported to overcome this problem. First, an automated region segmentation of every OCT image according to the intensity variation along the vertical direction is proposed. In the second step, a morphological analysis of the segmented OCT images is employed for quantifying the features that could serve for tissue classification. This way of extracting features from the original images has been successfully employed for feature extraction for breast tissue density classification in mammographic CAD systems [20

20. A. Olivier, J. Freixenet, R. Marti, J. Pont, E. Pere, E.R.E. Denton, and R. Zwiggelaar, “A novel breast tisue density classification methodology,” IEEE T. Inf. Technol. Biomed. 12, 55–64 (2008). [CrossRef]

] or automated segmentation based upon remitted scatter spectra from pathologically distinct tumor regions [21

21. P.B. Garcia-Allende, V. Krishnaswamy, P.J. Hoopes, K.S. Samkoe, O.M. Conde, and B.W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009). [CrossRef] [PubMed]

]. To the authors’ knowledge, morphological analysis, however, has not previously been performed on OCT images. The proposed methodology will be qualitatively and quantitatively compared with textural analyses to show that it surpasses their capabilities in gastrointestinal tissue discrimination.

2. Materials and methods

2.1. OCT instrumentation

Surgical specimens were imaged employing a commercial SS-OCT system (OCS1300SS, Thorlabs Incorporated, Newton, New Jersey), which incorporates a high-speed frequency swept external cavity laser (1325 nm central wavelength) having a 3 dB spectral bandwidth (> 100 nm) and an average output power of 10 mW. The frequency clock for the laser is provided by a built-in Mach-Zehnder Interferometer (MZI, Thorlabs INT-MZI-1300) and the main output of the laser is coupled into a fiber-based Michelson interferometer and split into the reference and sample arm using a 50/50 coupler (Thorlabs FC1310-70-50-APC).

In the reference arm of the interferometer, the light is reflected back into the fiber by a stationary mirror. In the sample arm, it is fiber coupled into the microscope head, collimated and then directed by the XY galvo scanning mirrors towards the sample. The axial scans (A-scans) are performed at 16 kHz, which is the sweeping frequency of the laser. The transverse scan (B-scan) is controlled by the galvo scanning mirrors and determines the frame rate of the OCT imaging. The sample is placed on a stage, providing XY and rotational translation. A pair of XY galvo mirrors sequentially scans the probe beam across the sample surface area, and the 3D volume data set under this area is acquired (C-scan).

This OCT system produces high-resolution cross-sectional images of the gastrointestinal tissues with axial and transverse resolution of 9 and 15 μm, respectively. The interference signal is detected using a high-impedance gain balance photodetector having a provision for noise correction. The fast Fourier transform (FFT) is used to convert the time to frequency of the interference signal. However, raw OCT interference fringe signals in the time domain are recorded using the software package within the SS-OCT system. Time to frequency domain conversion to obtain the depth-dependent reflectivity profile for the OCT image is subsequently performed with Matlab 7.9.0.529 (R2009b) that is also utilized off-line for image enhancement and further processing.

2.2. Gastrointestinal tissue surgical specimens

Specimens from gastrointestinal surgery are generally quite large, as they consist of large parts of - or even whole - organs, such as the oesophagus, stomach, large bowel or rectum. In advanced stages, where the tumour may have spread to neighbouring organs, they, or parts thereof, may also be included, such as the tip of the pancreas or even the whole spleen. Normally, these specimens are placed in formalin as soon as they are excised, along with any other tissues, such as lymph nodes, unless the surgeon wishes to open the specimen, for example to visually assess the completeness of excision.

For the purposes of this study, specimens were collected from theaters in warm normal saline (0.9% sodium chloride) to maintain hydration. Formalin was not used until after imaging, as it is a fixative, causing cross-linking of proteins and effectively changing the structural properties of tissues and, consequently, their optical properties too. The specimens were collected as soon as they were excised and immediately taken to the histology lab. There, they were gently rinsed exposing the mucosa and any lesions. In order to stabilise them for imaging and mark sites of interest, specimens were pinned onto corkboards. Large pins were used to secure the specimen and smaller ones to mark areas from which OCT imaging was carried out, apart from tumour sites, where the thickness of the wall and tumour would not allow the pins to reach the cork-board. The specimens were then carried to the OCT lab in a closed tray. Since they are much thicker than the penetration depth of the OCT beam, they do not require any special mounting and were imaged directly on the corkboard. After imaging was complete (within 30 minutes of resection), tissues were fixed with 10% formalin and returned to pathology within one hour of excision, for routine histological processing, which included paraffin embedding, sectioning and Hematoxylin/Eosin (H/E) staining.

For each site that was imaged, a letter was appended to a unique patient code, starting from “A” for the first site imaged and carrying on in alphabetical order. This allowed posterior correlation with the histological and automated classification results, as well as any intended patient per patient analysis. A total of 35 sites from 11 patients that were 3 × 3 mm in size were imaged. Nine of them corresponded to tumour sites (belonging to 7 different patients), while the remaining imaged sites included stomach (20 sites from 9 patients), and oesophagus (6 sites from 6 patients). The sample population is, therefore, unevenly distributed across patients, meaning that not only the number of imaged sites per diagnostic category varied, but also the tissue types imaged per patient. A 3D volume data set (C-scan) was obtained per imaged site. The OCT software always generated data to a fixed depth of 3 mm, regardless of the on-screen depth set by the user, which was for viewing purposes only. Consequently, the other two dimensions were fixed to 3 mm length to obtain a cube-shaped C-scan. The lateral resolution was set to 512 pixels which implies that each C-scan consists of 512 transverse OCT images (B-scans). As the axial resolution of the system was also fixed at 512 pixels, the resulting OCT images contains 512 axial scans (A-scans) each.

2.3. Image enhancement

Raw OCT images suffer from eventual outliers (e.g. reflection artifacts) that need to be corrected or removed. Additionally, intensity information above the surface also has to be discarded. Reflections caused by the beam angle provoke brighter pixels at certain depth positions such as the top of the image as well as on the surface, and these were employed for detecting and removing the intensity depth profiles at these pixel localizations. A ribbon of “air” at the top of the image was considered and the mean intensity of this ribbon was calculated for every A-scan. Only those A-scans whose mean intensity in the ribbon was higher than the 75th quartile plus 1.5× the interquartile range of the mean intensities along the whole B-scan were discarded as reflection artifacts but the rest of the axial scans that comprise the OCT image were maintained. Regarding surface detection, there is no established method for unsupervised surface recognition [18

18. F. Bazant-Hegemark and N. Stone, “Near real-time classifiation of optical coherence tomography data using principal component analysis fed linear discriminant analysis,” J. Biomed. Opt. 13, 034002 (2008). [CrossRef] [PubMed]

]. The highest intensity within one A-scan is not necessarily correlated with the sample surface, and neither is the largest change in intensity if the first derivative is taken. Consequently, a variety of methods for surface recognition in OCT have been previously reported, such as erosion/dilation techniques based on a binary threshold image [3

3. X. Qi, M.V. Sivak Jr., G. Isenberg, J.E. Willis, and A.M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006). [CrossRef] [PubMed]

], shapelet-based boundary recognition [22

22. J. Rogowska, T. Hancewicz, and P. Kaplan, “Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis,” Opt. Express 12, 5760–5769 (2004). [CrossRef]

] or rotation kernel transformations [23

23. J. Rogowska, C.M. Bryant, and M.E. Breinski, “Cartilage thicknes measurements from optical coherence tomography,” J. Opt. Soc. Am. A 20, 357–367 (2003). [CrossRef]

]. A binary thresholding approach as reported in [18

18. F. Bazant-Hegemark and N. Stone, “Near real-time classifiation of optical coherence tomography data using principal component analysis fed linear discriminant analysis,” J. Biomed. Opt. 13, 034002 (2008). [CrossRef] [PubMed]

] was selected because of its accuracy and simplicity. Again a ribbon of “air” above the surface was considered and its mean plus 0.75× its standard deviation was used as the intensity threshold. This threshold cuts off low intensity signals from deeper areas, therefore providing noise-free images. The 95th quartiles of these noise-free images were determined and employed for surface detection. Finally, every OCT image was wrapped, i.e. aligned with respect to the orientation of the extracted surface. Figure 1 summarizes the whole image preprocessing and shows the obtained enhanced image in a sample tomogram.

Fig. 1 Flow diagram of OCT image preprocessing stages and visualization of the full process on a sample image.

2.4. Morphological analysis in optical coherence tomography images

Fig. 2 Schematic of the proposed two-step methodology for feature quantification of OCT images.

The ability of the proposed morphological approach to extract the features that serve for tissue classification is qualitatively and quantitatively compared with textural approaches previously reported in this regard. The qualitative comparison is accomplished in terms of the clustering degree of the classes observed in the scatter plots. However, class separabilities measured in terms of the quotient between the traces of the between- and within-class scatter matrices are also indicated in the plots:
J=tr(Qb)tr(Qw),
(2)
where “tr” denotes the trace of a matrix, i.e. the sum of the diagonal elements, Qb is the between-class scatter matrix, and Qw is the within-class scatter matrix, estimated as follows:
Qb=i=1CPi(viv)(viv)T,Qw=i=1CPi1nik=1ni(xikvi)(xikvi)T,
(3)
where xik (k = 1,...,ni) are the elements from the ith class, vi is the mean of the vectors in the ith class, v the mean of the centers and C the number of classes which possess a priori class probability Pi (i = 1,...,C) and cardinality ni (i = 1,...,C).

Separability measures based on scatter matrices are normally preferable to probabilistic distances because they do not require an estimation of the probability density function of the classes or that those probability density functions are known a priori. The average distance between the elements of the classes can be expressed as the sum of the traces of their between-and within-class scatter matrices [25

25. P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice Hall, London, 1982).

]. However, the quotient between the traces is employed instead of their sum because it reflects more the intuitive notion of maximizing the trace of the between-class matrix while simultaneously minimizing the trace of the within-class matrix [26

26. D. Tikk and K.W. Wong, “A feature ranking technique based on interclass separability for fuzzy modeling,” in Proceedings of IEEE International Computational Cybernetics ICCC 2007 (Academic, Gammarth, Tunisia, 2007), pp. 251–256. [CrossRef]

]. The quantitative comparison is based on a classification criterion, i.e. the objective function is the classification accuracy attained with a pattern recognition algorithm, as described in Section 2.5.

2.5. Classification and validation

A KNN classifier [27

27. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic Press, New York, 1990).

] is employed for ready discrimination between gastrointestinal tissues employing the morphological features of the segmented OCT images. For comparison, textural features are also interpreted for tissue characterization. In both cases, an unclassified tomogram (herein referred to as the query point) represented by a vector in a d-dimensional space, where d depends on the number of segmented regions if morphological features are employed or on the number of concatenated textural features, is assigned to the majority diagnosis of its K-nearest vectors found in the feature space. This approach can naturally deal with multiclass data while some of the more advanced classifiers, such as support vector machines (SVM) [28

28. V. Vapnik, The nature of Statistical Learning Theory (Springer, New York, 1995).

], require the bridging of results from a combinatorial set of such classifiers to simulate multiclass parameters [20

20. A. Olivier, J. Freixenet, R. Marti, J. Pont, E. Pere, E.R.E. Denton, and R. Zwiggelaar, “A novel breast tisue density classification methodology,” IEEE T. Inf. Technol. Biomed. 12, 55–64 (2008). [CrossRef]

]. Accuracy of the KNN classifier mostly depends on the metric used to compute distances between the query point and all training pixels in the feature space. Extracted features varied greatly and, therefore, they were normalized to prevent some features from being more strongly weighted than others. All features were statistically normalized to zero mean and unit variance employing a combined mean feature vector (μ) and a combined standard deviation vector (σ) from the training data set. The employment of the Euclidean distance,
D(x,y)2=(yx)TM(yx),
(4)
where x and y are the two compared tomograms and M is the identity matrix, is the most common and simplest approach for measuring the separation between the query point and the training data when no prior knowledge about the probability density function of a particular class (tissue type) is available. Since it assumes that all features that define a tomogram are equally important and independent from others [29

29. D. A. Burns and E.W. Ciurczak, Handbook of Near-Infrared Analysis, 3rd ed. (CRC Press, 2008), Chap. 15.

], it will not be the ideal metric when diagnostic categories are, for example, elongated in some directions. This difficulty has been overcome alternatively employing the Mahalanobis distance which is given in Eq. 4, where M is the covariance matrix for the extracted features. If all the features were uncorrelated and they had the same variance, the computation of the Mahalanobis distance would be equivalent to the Euclidean metric.

Apart from the distance criterion, the behaviour of the classifier depends on the number of nearest neighbors (K). Classification accuracy should be expected to increase with K because this reduces the influence of training data points assigned to a wrong diagnostic category. However, the error percentage is also influenced by the spreading of the extracted features within classes [21

21. P.B. Garcia-Allende, V. Krishnaswamy, P.J. Hoopes, K.S. Samkoe, O.M. Conde, and B.W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009). [CrossRef] [PubMed]

].

In order to accurately estimate the performance of the feature extraction methodology in comparison with textural approaches a threefold cross-validation technique or procedure [30

30. H. Zhu and R. Rohwer, “No free lunch for cross-validation,” Neural Comput. 8, 1421–1426 (1996). [CrossRef]

, 31

31. C. Goutte, “Note on free lunches and cross validation,” Neural Comput. 9, 1211–1215 (1997). [CrossRef]

] was applied. B-scans across the whole data set were randomly divided into three nonoverlapping sets, with roughly equal size. Two of these sets were employed as a training set (used to populate feature space), and the other was employed as a validation set (query points) to compute the accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) per diagnostic category from all others. This procedure is repeated three times, each time with different training and validation sets. Finally, the estimated performance of the classifier was calculated by averaging the three resulting errors. This approach eliminates the dependency of classification results on the training or test sets. Additionally, a leave-one-patient-out procedure is accomplished to show that the proposed disease markers, i.e. the morphological features, are greater than interpatient variation and the approach is feasible for gastrointestinal tissue classification in the clinical setting. Because of the moderate sample size, the validation set consists of all the C-scans from one patient while C-scans from other patients populate the feature space. Therefore, B-scans pertaining to the same C-scan were split across training and validation sets in the cross-validation while all B-scans comprising all C-scans from one patient were kept together and classified using all C-scans from other patients in the leave-one-patient-out procedure. As in the aforementioned cross-validation analysis, the leave-one-patient-procedure is, however, repeated as many times as the number of patients and reported measures were calculated by averaging the resulting sensitivity and specificity values per tissue type. In this second validation process, B-scans are not equally distributed in either the training or testing sets. Classification measures are, therefore, influenced by the heterogeneity of the sample population, but it is more realistic in a clinical scenario than the cross-validation employed in the comparison of image parameter extraction approaches.

3. Results

3.1. Qualitative comparison of feature extraction strategies

Fig. 3 Grouped scatter plots and interclass separabilities (J) for the distinct gastrointestinal tissues depending on the number of segmented regions (only B-scans comprising all C-scans from a sample patient are included).
Fig. 4 Grouped scatter plots and interclass separabilities J for the distinct gastrointestinal tissues depending on the approach employed for image feature quantification (The map is populated with all data points from a sample patient).

The technique employed in the quantification of features also needs to deal with interpatient variability because these features need to serve for tissue classification in the clinical setting. As shown in Fig. 4 (a and c), individual spatial frequency texture analysis, i.e. based on the DFT, and CSAC-based statistical texture analysis exhibit limited performance when dealing with the intrapatient variability and, consequently, different types are combined before their employment in computer-aided diagnosis of GI diseases as described in the Introduction. The behaviour of SGLDM-based statistical texture analysis was significantly better (J = 9.58) and it approaches to the interclass separability attained with morphological features (J = 15.07), which maximize the ratio between the within-class and between-class variances in this pilot study using a restricted patient population. Therefore, it is expected that they will have a better performance when data from different patients are considered. The validity of this intuitive assumption is demonstrated in a clinical investigation with a larger population of patients, i.e. the whole data set described above, and the results of this investigation are depicted in Figure 5. A tendency to group is still observed in the scatter plot, indicating the ability of the approach to reasonably differentiate diagnostic categories. Although the overlap among these categories is also noticeable, and, as a consequence, the interclass separability drops significantly, the quantitative comparison in the following section will demonstrate that the further processing of the morphological features with the KNN classifier provides reliable tissue categorization in clinical settings.

Fig. 5 3D feature space assembled with the PCA-processed morphological features (3 segmented regions) from all B-scans that comprise the whole data set and its corresponding interclass separability (J).

3.2. Quantitative comparison of feature extraction strategies

The accuracy of the proposed morphological approach for the quantification of OCT image features fundamentally depends on the number of segmented regions in the axial direction. If KNN classification is applied to the extracted features to understand the relationship between the image parameters, and to classify new data, there is an additional tunable variable that influences tissue discrimination capability, i.e. the number of neighbors (K to differentiate it from the number of regions for axial segmentation (k)). The best choice of the number of neighbors depends upon the data; generally, larger values of neighbors reduce the effect of noise on the classification, but make boundaries between classes less distinct [15

15. X. Qi, Y. Pan, S.V. Sivak Jr., J.E. Willis, G. Isenberg, and A.M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomedical Optics Express 1, 825–847 (2010). [CrossRef]

]. As shown in the grouped scatter plot in Fig. 5, different gastrointestinal tissues exhibit a tendency to group but their degree of overlap is also relevant. Therefore, a multivariate analysis that establishes sharp boundaries among classes is sought and, accordingly, one nearest neighbor is employed. Once the number of neighbors is fixed, a direct comparison of the number of segmented regions can be accomplished.

Figure 6 graphically compares the attained specificity and sensitivity values for varying number of segmented regions. As described previously, these measures are based on the ability to discriminate a given tissue type from all other categories evaluated and were averaged for all possible permutations of training and test sets in the cross validation procedure. Classification accuracy first increases when OCT images are vertically segmented into two regions since these predictions are closer to the representation of the perfect classifier (sensitivity:specificity of 100%:100%). Differences in tissue discrimination capability are hardly noticeable for a number of segmented regions varying between 2 and 5, and then accuracy decreases again for a higher number of regions. In terms of the computational load, a reduced number of regions is preferable, because this indicates that accurate classification is performed with a smaller number of image parameters. Consequently, only two regions will be further considered for the comparison with textural approaches.

Fig. 6 Specificity and sensitivity values per tissue type as a function of the number of segmented regions.

An identical approach is followed for comparing morphological analysis with previously reported textural approaches and this comparison is shown in Figure 7. SGLDM features offer the best discrimination capability of gastrointestinal tissues among textural approaches but all of them are greatly surpassed by the proposed two-step methodology. Table 1 presents the complete results of this quantitative comparison measured as the obtained sensitivity, specificity, PPV, NPV and accuracy for discriminating a tissue type from all others.

Fig. 7 Comparison of morphological and textural prediction in terms of their sensitivity and specificity values in the discrimination of each tissue type.

Table 1. Summary of the quantitative comparison between morphological and textural approaches for feature quantification of OCT images in the classification of gastrointestinal tissues.

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Table 2. Summary of the efficacy of the KNN classifier (K = 1) to understand the relationship between the image features, and to predict new data. Reported measures are the sensitivity:specificity values in the discrimination of a given gastrointestinal tissue type from all other evaluated types.

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4. Discussion and conclusions

The feasibility of texture analysis of optical coherence tomography images for tissue classification relies on the quantification of the loss of structure associated with normal histological organization. Meanwhile, the proposed method is based on an evaluation of the intensity distribution in different regions along the vertical axis, which allows a statistical comparison of the extent of signal in different tissue types. Recent studies have proposed to employ the attenuation coefficient (μt), which describes the decay of detected light intensity with depth [32

32. E.C. Cauberg, D.M. de Bruin, D.J. Faber, T.M. de Reijke, M. Visser, J.J. de la Rosette, and T.G. van Leeuwen, “Quantitative measurement of attenuation coefficients of bladder biopsies using optical coherence tomography for grading urothelial carcinoma of the bladder,” J. Biomed. Opt. 15, 066013 (2010). [CrossRef]

,33

33. K. Barwari, D.M. de Bruin, E.C. Cauberg, D.J. Faber, T.G. van Leeuwen, H. Wijkstra, J.J. de la Rosette, and M.P. Laguna, “Advanced diagnostics in renal mass using optical coherence tomography: A preliminary report,” J. Endourol. 25, 311–315 (2011). [CrossRef] [PubMed]

]. In malignant tissue displaying larger and irregularly shaped nuclei compared with normal tissue, light scattering is expected to be larger resulting in changes in μt. Statistically significant differences between normal renal tissue and renal cell carcinoma were reported in [33

33. K. Barwari, D.M. de Bruin, E.C. Cauberg, D.J. Faber, T.G. van Leeuwen, H. Wijkstra, J.J. de la Rosette, and M.P. Laguna, “Advanced diagnostics in renal mass using optical coherence tomography: A preliminary report,” J. Endourol. 25, 311–315 (2011). [CrossRef] [PubMed]

], but the differences encountered between benign bladder tissue and bladder urothelial carcinoma [32

32. E.C. Cauberg, D.M. de Bruin, D.J. Faber, T.M. de Reijke, M. Visser, J.J. de la Rosette, and T.G. van Leeuwen, “Quantitative measurement of attenuation coefficients of bladder biopsies using optical coherence tomography for grading urothelial carcinoma of the bladder,” J. Biomed. Opt. 15, 066013 (2010). [CrossRef]

] were not significant. Several environmental factors (orientation of the biopsy, cauterization effects, etc.) that might account for this lack of difference in μt among the different pathological types are mentioned. In this study, we assessed signal attenuation using a more sophisticated approach that is independent of environmental artifacts.

This better performance when using morphological features can also be appreciated in the results presented in Table 2 from the ‘clinical’ investigation. This has been accomplished through a leave-one-patient-out procedure and, consequently, is more relevant for establishing the validity of the proposal for the clinical environment. A sensitivity and specificity of 86.9% and 73.0% is obtained, which again greatly enhances that attained with textural approaches. The clinical reliability of the approach could be limited by the employment of the KNN classifier as the multivariate analysis to understand the relationship between the image parameters, and to predict new data. More sophisticated classification algorithms for the further processing of the extracted parameters, such as artificial neural networks (ANN) or classification trees, could aid to enhance achieved sensitivity and specificity values. However, KNN is sufficient to demonstrate the enhanced behaviour of morphological analysis with respect to textural strategies in extracting the OCT image parameters for tissue classification, as demonstrated both in the qualitative and quantitative comparisons accomplished in this study. This enhanced behaviour is presumably due to a greater sensitivity of the extent of light penetration into tissues to the morphological changes occurring in malignant tissue than the loss of structure quantified by texture features. This sensibility is strengthened by the region segmentation performed in the first stage of the proposed method. While texture features only quantify the loss of structure in a previously determined ROI, the variation of signal strength with depth is quantified independently in all the segmented regions allowing more detailed signal attenuation information to be retained. The benefit of this might be barely noticeable in the sensitivity and specificity values attained in the cross-validation procedure as a consequence of the overfitting of the algorithm, but it is particularly relevant, however, for the ’clinical’ investigation, i.e. the leave-one-patient-out study. If no segmentation of the OCT images is accomplished specificity:sensitivity values of 69.74%:42.6% (tumor), 77.02%:59.97% (stomach) and 100%:36% (oesophagus) are obtained, which are much worse than if the OCT images are segmented into two regions, as indicated in Table 2. This confirms the necessity of segmenting the tomograms in the vertical direction to enhance the effect of smaller light penetration in tumours, or the brightness of oesophagus on the extracted features beyond the inter-patient variability.

Ongoing studies are further investigating the clinical validity of the methodology. This includes the accomplishment of a blind study with a much larger population of patients. Apart from that, the discrimination capability has only been assessed for gastrointestinal tissues. Accordingly, future research lines are also intended to demonstrate the suitability of the proposed feature extraction approach from OCT images for tissue classification in other medical applications, specifically urology.

Acknowledgments

Funding is gratefully acknowledged from the ERC grant StG 242991. This work was also partly funded by the National Physical Laboratory, Teddington, Middlesex, UK.

References and links

1.

D. Huang, E.A. Swanson, C.P. Lin, J.S. Schuman, W.G. Stinson, W. Chang, H.R. Hee, F. Flotte, K. Gregory, C.A. Puliafito, and J.G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991). [CrossRef] [PubMed]

2.

J.A. Izatt, M.D. Kulkarni, H.W. Wang, K. Kobayashi, and M.V. Sivak Jr., “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2, 1017–1028 (1996). [CrossRef]

3.

X. Qi, M.V. Sivak Jr., G. Isenberg, J.E. Willis, and A.M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006). [CrossRef] [PubMed]

4.

R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express 11, 889–894 (2003). [CrossRef] [PubMed]

5.

M. Choma, M. Sarunic, C. Yang, and J.A. Izatt, “Sensitivity advantage of swept source and Fourier domain optical coherence tomography,” Opt. Express 11, 2183–2189 (2003). [CrossRef] [PubMed]

6.

S. Yun, G. Tearney, B. Bouma, B. Park, and J. de Boer, “High-speed spectral-domain optical coherence tomography at 1.3 μm wavelength,” Opt. Express 11, 3598–3604 (2003). [CrossRef] [PubMed]

7.

G.J. Tearney, M.E. Brezinski, B.E. Bouma, S.A. Boppart, C. Pitvis, J.F. Southern, and J.G. Fujimoto, “In vivo endoscopic optical biopsy with optical coherence tomography (OCT): a review,” Science 276, 2037–2039 (1997). [CrossRef] [PubMed]

8.

A.M. Sergeev, V.M. Gelikonov, G.V. Gelikonov, F.I. Feldchtein, R.V. Kuranov, N.D. Gladkova, N.M. Shakhova, L.B. Suopova, A.V. Shakhov, I.A. Kuznetzova, A.N. Denisenko, V.V. Pochinko, Y.P. Chumakov, and O.S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. Express I, 432–440 (1997). [CrossRef]

9.

J.M. Poneros, S. Brand, B.E. Bouma, G.J. Tearney, C.C. Compton, and N.S. Nishiosa, “Diagnosis of specialized intestinal metaplastia by optical coherence tomography,” Gastroenterology 120, 7–12 (2001). [CrossRef] [PubMed]

10.

G. Isenberg, M.V. Sivak Jr., A. Chak, R.C.K. Wong, J.E. Willis, B. Wolf, D.Y. Rowland, A. Das, and A. Rollins, “Accuracy of endoscopic optical coherence tomography in the detection of dysplasia in Barrett’s esophagus: a prospective, doubled-blinded study,” Gastrointest. Endosc. 62, 825–831 (2005). [CrossRef] [PubMed]

11.

P.R. Pfau, M.V. Sivak Jr., A. Chak, M. Kinnard, R.C. Wong, G.A. Isenberg, J.A. Izatt, A. Rollins, and V. Westphal, “Criteria for diagnosis of dysplasia by endoscopic optical coherence tomography,” Gastrointest. Endosc. 58, 196–202 (2003). [CrossRef] [PubMed]

12.

K.W. Gossage, J.J. Rodriguez, and J.K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003). [CrossRef] [PubMed]

13.

K.W. Gossage, C.M. Smith, E.M. Kanter, L.P. Hariri, A.L. Stone, J.J. Rodriguez, S.K. Williams, and J.K. Barton, “Texture analysis of speckle in optical coherence tomography images of tissue phantoms,” Phys. Med. Biol. 51, 1563–1575 (2006). [CrossRef] [PubMed]

14.

Y. Chen, A.D. Aguirre, P.L. Hsiung, S.W. Huang, H. Mashimo, J.M. Schmitt, and J.G. Fujimoto, “Effects of axial resolution improvement on optical coherence tomography (OCT) imaging of gastrointestinal tissues,” Opt. Express 16, 2469–2485 (2008). [CrossRef] [PubMed]

15.

X. Qi, Y. Pan, S.V. Sivak Jr., J.E. Willis, G. Isenberg, and A.M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomedical Optics Express 1, 825–847 (2010). [CrossRef]

16.

D. Harwood, T. Ojala, M. Pietikainen, S. Kelman, and L. Davis, “Texture classification by center-symmetric auto-correlation using Kullback discrimination of distributions,” Pattern Recognit. Lett. 16, 1–10 (1995). [CrossRef]

17.

I.T. Jolliffe, Principal Component Analysis, 2nd ed. (Springer, 2002).

18.

F. Bazant-Hegemark and N. Stone, “Near real-time classifiation of optical coherence tomography data using principal component analysis fed linear discriminant analysis,” J. Biomed. Opt. 13, 034002 (2008). [CrossRef] [PubMed]

19.

A. Barui, P. Banerjee, R. Patra, R.K. Das, S. Dhara, P.K. Dutta, and J. Chatterjee,“Swept-source optical coherence tomography of lower limb wound healing with histopathological correlation,” J. Biomed. Opt. 16, 0260101–0260108 (2011). [CrossRef]

20.

A. Olivier, J. Freixenet, R. Marti, J. Pont, E. Pere, E.R.E. Denton, and R. Zwiggelaar, “A novel breast tisue density classification methodology,” IEEE T. Inf. Technol. Biomed. 12, 55–64 (2008). [CrossRef]

21.

P.B. Garcia-Allende, V. Krishnaswamy, P.J. Hoopes, K.S. Samkoe, O.M. Conde, and B.W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009). [CrossRef] [PubMed]

22.

J. Rogowska, T. Hancewicz, and P. Kaplan, “Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis,” Opt. Express 12, 5760–5769 (2004). [CrossRef]

23.

J. Rogowska, C.M. Bryant, and M.E. Breinski, “Cartilage thicknes measurements from optical coherence tomography,” J. Opt. Soc. Am. A 20, 357–367 (2003). [CrossRef]

24.

R. Ghanadesikan, Methods for Statistical Data Analysis of Multivariate Observation (Wiley, New York, 1997). [CrossRef]

25.

P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice Hall, London, 1982).

26.

D. Tikk and K.W. Wong, “A feature ranking technique based on interclass separability for fuzzy modeling,” in Proceedings of IEEE International Computational Cybernetics ICCC 2007 (Academic, Gammarth, Tunisia, 2007), pp. 251–256. [CrossRef]

27.

K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic Press, New York, 1990).

28.

V. Vapnik, The nature of Statistical Learning Theory (Springer, New York, 1995).

29.

D. A. Burns and E.W. Ciurczak, Handbook of Near-Infrared Analysis, 3rd ed. (CRC Press, 2008), Chap. 15.

30.

H. Zhu and R. Rohwer, “No free lunch for cross-validation,” Neural Comput. 8, 1421–1426 (1996). [CrossRef]

31.

C. Goutte, “Note on free lunches and cross validation,” Neural Comput. 9, 1211–1215 (1997). [CrossRef]

32.

E.C. Cauberg, D.M. de Bruin, D.J. Faber, T.M. de Reijke, M. Visser, J.J. de la Rosette, and T.G. van Leeuwen, “Quantitative measurement of attenuation coefficients of bladder biopsies using optical coherence tomography for grading urothelial carcinoma of the bladder,” J. Biomed. Opt. 15, 066013 (2010). [CrossRef]

33.

K. Barwari, D.M. de Bruin, E.C. Cauberg, D.J. Faber, T.G. van Leeuwen, H. Wijkstra, J.J. de la Rosette, and M.P. Laguna, “Advanced diagnostics in renal mass using optical coherence tomography: A preliminary report,” J. Endourol. 25, 311–315 (2011). [CrossRef] [PubMed]

34.

A.M. Laughney, V. Krishnaswamy, P.B. Garcia-Allende, O.M. Conde, W.A. Wells, K.D. Paulse, and B.W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt. 15, 066019 (2010). [CrossRef]

OCIS Codes
(110.2960) Imaging systems : Image analysis
(110.4500) Imaging systems : Optical coherence tomography
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Image Processing

History
Original Manuscript: August 4, 2011
Revised Manuscript: September 9, 2011
Manuscript Accepted: September 15, 2011
Published: September 22, 2011

Virtual Issues
Advances in Optics for Biotechnology, Medicine, and Surgery (2011) Biomedical Optics Express

Citation
P. Beatriz Garcia-Allende, Iakovos Amygdalos, Hiruni Dhanapala, Robert D. Goldin, George B. Hanna, and Daniel S. Elson, "Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues," Biomed. Opt. Express 2, 2821-2836 (2011)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-2-10-2821


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References

  1. D. Huang, E.A. Swanson, C.P. Lin, J.S. Schuman, W.G. Stinson, W. Chang, H.R. Hee, F. Flotte, K. Gregory, C.A. Puliafito, and J.G. Fujimoto, “Optical coherence tomography,” Science254, 1178–1181 (1991). [CrossRef] [PubMed]
  2. J.A. Izatt, M.D. Kulkarni, H.W. Wang, K. Kobayashi, and M.V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron.2, 1017–1028 (1996). [CrossRef]
  3. X. Qi, M.V. Sivak, G. Isenberg, J.E. Willis, and A.M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt.11, 044010 (2006). [CrossRef] [PubMed]
  4. R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express11, 889–894 (2003). [CrossRef] [PubMed]
  5. M. Choma, M. Sarunic, C. Yang, and J.A. Izatt, “Sensitivity advantage of swept source and Fourier domain optical coherence tomography,” Opt. Express11, 2183–2189 (2003). [CrossRef] [PubMed]
  6. S. Yun, G. Tearney, B. Bouma, B. Park, and J. de Boer, “High-speed spectral-domain optical coherence tomography at 1.3 μm wavelength,” Opt. Express11, 3598–3604 (2003). [CrossRef] [PubMed]
  7. G.J. Tearney, M.E. Brezinski, B.E. Bouma, S.A. Boppart, C. Pitvis, J.F. Southern, and J.G. Fujimoto, “In vivo endoscopic optical biopsy with optical coherence tomography (OCT): a review,” Science276, 2037–2039 (1997). [CrossRef] [PubMed]
  8. A.M. Sergeev, V.M. Gelikonov, G.V. Gelikonov, F.I. Feldchtein, R.V. Kuranov, N.D. Gladkova, N.M. Shakhova, L.B. Suopova, A.V. Shakhov, I.A. Kuznetzova, A.N. Denisenko, V.V. Pochinko, Y.P. Chumakov, and O.S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. ExpressI, 432–440 (1997). [CrossRef]
  9. J.M. Poneros, S. Brand, B.E. Bouma, G.J. Tearney, C.C. Compton, and N.S. Nishiosa, “Diagnosis of specialized intestinal metaplastia by optical coherence tomography,” Gastroenterology120, 7–12 (2001). [CrossRef] [PubMed]
  10. G. Isenberg, M.V. Sivak, A. Chak, R.C.K. Wong, J.E. Willis, B. Wolf, D.Y. Rowland, A. Das, and A. Rollins, “Accuracy of endoscopic optical coherence tomography in the detection of dysplasia in Barrett’s esophagus: a prospective, doubled-blinded study,” Gastrointest. Endosc.62, 825–831 (2005). [CrossRef] [PubMed]
  11. P.R. Pfau, M.V. Sivak, A. Chak, M. Kinnard, R.C. Wong, G.A. Isenberg, J.A. Izatt, A. Rollins, and V. Westphal, “Criteria for diagnosis of dysplasia by endoscopic optical coherence tomography,” Gastrointest. Endosc.58, 196–202 (2003). [CrossRef] [PubMed]
  12. K.W. Gossage, J.J. Rodriguez, and J.K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt.8, 570–575 (2003). [CrossRef] [PubMed]
  13. K.W. Gossage, C.M. Smith, E.M. Kanter, L.P. Hariri, A.L. Stone, J.J. Rodriguez, S.K. Williams, and J.K. Barton, “Texture analysis of speckle in optical coherence tomography images of tissue phantoms,” Phys. Med. Biol.51, 1563–1575 (2006). [CrossRef] [PubMed]
  14. Y. Chen, A.D. Aguirre, P.L. Hsiung, S.W. Huang, H. Mashimo, J.M. Schmitt, and J.G. Fujimoto, “Effects of axial resolution improvement on optical coherence tomography (OCT) imaging of gastrointestinal tissues,” Opt. Express16, 2469–2485 (2008). [CrossRef] [PubMed]
  15. X. Qi, Y. Pan, S.V. Sivak, J.E. Willis, G. Isenberg, and A.M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomedical Optics Express1, 825–847 (2010). [CrossRef]
  16. D. Harwood, T. Ojala, M. Pietikainen, S. Kelman, and L. Davis, “Texture classification by center-symmetric auto-correlation using Kullback discrimination of distributions,” Pattern Recognit. Lett.16, 1–10 (1995). [CrossRef]
  17. I.T. Jolliffe, Principal Component Analysis, 2nd ed. (Springer, 2002).
  18. F. Bazant-Hegemark and N. Stone, “Near real-time classifiation of optical coherence tomography data using principal component analysis fed linear discriminant analysis,” J. Biomed. Opt.13, 034002 (2008). [CrossRef] [PubMed]
  19. A. Barui, P. Banerjee, R. Patra, R.K. Das, S. Dhara, P.K. Dutta, and J. Chatterjee,“Swept-source optical coherence tomography of lower limb wound healing with histopathological correlation,” J. Biomed. Opt.16, 0260101–0260108 (2011). [CrossRef]
  20. A. Olivier, J. Freixenet, R. Marti, J. Pont, E. Pere, E.R.E. Denton, and R. Zwiggelaar, “A novel breast tisue density classification methodology,” IEEE T. Inf. Technol. Biomed.12, 55–64 (2008). [CrossRef]
  21. P.B. Garcia-Allende, V. Krishnaswamy, P.J. Hoopes, K.S. Samkoe, O.M. Conde, and B.W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt.14, 034034 (2009). [CrossRef] [PubMed]
  22. J. Rogowska, T. Hancewicz, and P. Kaplan, “Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis,” Opt. Express12, 5760–5769 (2004). [CrossRef]
  23. J. Rogowska, C.M. Bryant, and M.E. Breinski, “Cartilage thicknes measurements from optical coherence tomography,” J. Opt. Soc. Am. A20, 357–367 (2003). [CrossRef]
  24. R. Ghanadesikan, Methods for Statistical Data Analysis of Multivariate Observation (Wiley, New York, 1997). [CrossRef]
  25. P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice Hall, London, 1982).
  26. D. Tikk and K.W. Wong, “A feature ranking technique based on interclass separability for fuzzy modeling,” in Proceedings of IEEE International Computational Cybernetics ICCC 2007 (Academic, Gammarth, Tunisia, 2007), pp. 251–256. [CrossRef]
  27. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic Press, New York, 1990).
  28. V. Vapnik, The nature of Statistical Learning Theory (Springer, New York, 1995).
  29. D. A. Burns and E.W. Ciurczak, Handbook of Near-Infrared Analysis, 3rd ed. (CRC Press, 2008), Chap. 15.
  30. H. Zhu and R. Rohwer, “No free lunch for cross-validation,” Neural Comput.8, 1421–1426 (1996). [CrossRef]
  31. C. Goutte, “Note on free lunches and cross validation,” Neural Comput.9, 1211–1215 (1997). [CrossRef]
  32. E.C. Cauberg, D.M. de Bruin, D.J. Faber, T.M. de Reijke, M. Visser, J.J. de la Rosette, and T.G. van Leeuwen, “Quantitative measurement of attenuation coefficients of bladder biopsies using optical coherence tomography for grading urothelial carcinoma of the bladder,” J. Biomed. Opt.15, 066013 (2010). [CrossRef]
  33. K. Barwari, D.M. de Bruin, E.C. Cauberg, D.J. Faber, T.G. van Leeuwen, H. Wijkstra, J.J. de la Rosette, and M.P. Laguna, “Advanced diagnostics in renal mass using optical coherence tomography: A preliminary report,” J. Endourol.25, 311–315 (2011). [CrossRef] [PubMed]
  34. A.M. Laughney, V. Krishnaswamy, P.B. Garcia-Allende, O.M. Conde, W.A. Wells, K.D. Paulse, and B.W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15, 066019 (2010). [CrossRef]

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