2.1.

OCT

OCT is an imaging modality similar to ultrasound that uses partially coherent near-infrared light instead of sound to create images of subsurface structures. The origin of the received backscattered light is detected with interferometry; thus, a map of reflectivity versus optical depth and lateral position can be created. The core of the imaging system is a Michelson interferometer. The light from the optical source is split into two paths. One of the paths, the reference arm, consists of a delay line that varies the path length to create the depth information of the image. The other path contains the scanning arm, which sweeps the beam across the sample to obtain the lateral image information. When the path lengths of the light in each of the two arms are equal, they add constructively at the detector. These signals are then processed and used as the individual axial lines for the OCT image.²⁰

The axial and lateral resolutions of an OCT imaging system are decoupled. The lateral resolution is determined by the optics in the scanning arm, and the axial resolution is determined by the coherence length of the light source.²¹ The coherence length is inversely proportional to the spectral bandwidth of the light source; thus, for smaller coherence lengths, larger bandwidths are needed. Depending on the bandwidth, axial resolutions as low as $4\mu \mathrm{m}$ can be achieved, which is up to 25 times higher than the resolution of high-frequency ultrasound.²² When imaging, the axial and lateral resolutions are usually kept similar to maintain distance relationships in the image. However, for the lateral resolution to match the axial resolution when it drops below $10\mu \mathrm{m}$ , the focal length of the lens would need to be so small that the depth of focus of the system would drop. Consequently, most current catheter-based systems have axial resolutions between 10 and $20\mu \mathrm{m}$ .²² The actual axial resolution depends on the medium being imaged, but is proportional to the coherence length.

Tissue penetration depth in OCT is also a function of the wavelength of light used. For optical energy, tissue preferentially absorbs some wavelengths of light, while having very little effect on others. For instance, in the near-infrared range from $800\text{to}1300\mathrm{nm}$ , there is very little absorption in tissue.²³ For wavelengths that are not absorbed, scattering, which decreases at greater wavelengths, is the primary limitation of penetration depth. For this reason, OCT imaging has been investigated at several near-infrared wavelengths (830, 980, 1060, and $1300\mathrm{nm}$ ), depending on whether the emphasis is on resolution or tissue penetration.¹ At smaller wavelengths, resolution is higher, while at greater wavelengths tissue penetration is increased.

2.2.

OCT Image Analysis

The first OCT system was approved by the Food and Drug Administration for use in ophthalmology in 1993,²⁴ and the first commercial OCT system was introduced into the clinic in 1996.²⁴ Since then, many groups have developed algorithms to automatically detect the layers of the retina.^{2, 3, 4, 5} The thickness of the retina, which can be determined from OCT images, can help diagnose certain diseases and lead to more appropriate treatment.² Similarly, analysis of the ganglion cells and axons in the retinal nerve fiber layer (RNFL), which can be visualized using OCT, can help determine the progress of glaucoma.²

More recently, differences between OCT systems and the effect those differences have on measurements, such as the thickness of the RNFL, has become an active area of study. One of the first groups to make such comparisons was Bourne,²⁵ who imaged 139 subjects with two imaging systems and then compared the resulting RNFL thickness measurements. One of the systems generally produced thicker measurements; however, a correction factor was determined that, when applied, brought the measurements within $10\mu \mathrm{m}$ of each other for 75% of the patients. This difference is still large enough, however, that Bourne ²⁵ suggest that the measurements taken by these systems should be compared with caution. In a similar study, Sehi ²⁶ compared the measurements obtained by two different OCT systems when measuring not only the RNFL, but also the optic disk topography and central foveal thickness. As with the Bourne study, there were significant differences between measurements taken with the two systems; although in this study, no attempt was made to compensate for these differences. A warning was simply given that when comparing values between clinics or systems, system differences should be taken into account.

Research in the area of OCT image analysis is not limited to retinal measurements. OCT is actively being studied for the characterization of intravascular plaque^{6, 27} as well as for the recognition of cancer. Gossage and Tkaczyk²⁸ may have been the first to suggest that texture analysis can be used to analyze OCT images in an attempt to classify different tissue types. They were able to analyze the texture in OCT images to differentiate between in vitro images of mouse skin (correct classification rate of 98.5%) and testicular fat (97.3%), as well as normal lung (88.6%) and abnormal lung (64.0%). In a follow-up study, Gossage ¹⁹ used texture analysis and tissue phantoms to study the effect of the size and distribution of scatterers on the speckle present in OCT images. The results indicated that the change in size and distribution of scatterers did have a statistically significant effect on certain texture features, such as entropy, local homogeneity, inertia, and the low-frequency Fourier transform. In recent years, texture analysis has been used by a number of researchers in efforts to distinguish cancerous from noncancerous tissue. In two separate studies, Qi ^{10, 29} applied texture analysis along with other image analysis techniques to OCT images of the esophagus to diagnose dysplasia. The first study reported a sensitivity of 87% and a specificity of 69%, while the second study reported a sensitivity of 82% and a specificity of 74%. Zysk and Boppart created an algorithm using a combination of image and texture analysis techniques to recognize breast cancer.¹¹ The results of the study indicated that the combined algorithm had a tumor tissue sensitivity of 97% and a specificity of 68%. All these studies were carried out using data from only one system.

Previously, we have applied texture analysis to OCT images of the bladder to recognize the layers of the bladder,³⁰ and to differentiate cancerous from noncancerous tissue.¹² The study to differentiate cancerous from noncancerous bladder tissue reported a sensitivity of 92% and a specificity of 62%, using data from a single system. Unfortunately, the features selected turned out to be highly system dependent, and the algorithm overtrained due to the limited data used for algorithm development. It became apparent that for our algorithm to be applicable to multiple systems without requiring acquisition of training data on each new system, it needed to be designed with system independence in mind, causing us to begin considering methods of compensating for system differences. In addition, to avoid overtraining as we continued our research, we reverted to a simpler algorithm that required use of fewer features and would be influenced less by deviations present in the training data set. As more data become available, it should be possible to return to more complex algorithms capable of more reliable differentiation between tissue classes. The development of methods of accounting for system differences will not only allow developed algorithms to be applicable to more than a single imaging system, it will make it possible to use data from multiple data sets, developed on different systems, to develop more robust and complex algorithms.

Chen ³¹ conducted a study that used texture analysis to evaluate the ability of two systems having different resolutions to recognize Barrett’s esophagus. In vivo and in vitro tissues from the esophagus were analyzed with both imaging systems. One system had a center wavelength of $1310\mathrm{nm}$ and a resolution between 10 and $15\mu \mathrm{m}$ . The high-resolution system had a center wavelength of $1260\mathrm{nm}$ and a resolution of $<5\mu \mathrm{m}$ . The goal of the study was to determine whether the high-resolution system was able to improve the recognition of Barrett’s esophagus. The results indicated that the texture features calculated with the high-resolution system were able to better discriminate between normal tissue and Barrett’s esophagus. The paper does not mention, however, if the images taken with one system could be successfully analyzed using the images taken with the other as the training set.

Because of the limited availability of clinical OCT data in the area of cancer detection, recognition algorithms are being developed on single, small data sets. The reality, however, is that to develop robust algorithms capable of analyzing data from different systems, developers need multiple data sets from many different studies taken with different systems. If this is not the case, then the developed algorithms are likely to be limited to use on the system for which they were developed or to require training data gathered on each new system. To avoid having to acquire training data for every new system, we seek a method capable of compensating for system differences.

2.3.

OCT System Differences

There are a few parameters of OCT systems that are apparent when differentiating between systems, including resolution and depth of penetration, which are dependent on the central wavelength of the light source used. Systems with smaller wavelengths have better resolution but less penetration depth.

Other system differences include the signal-to-noise ratio, the range of intensity values, and the pixel size. The range of intensity values is often set at the time of imaging and may vary slightly from use to use. The general range would be dependent on the average intensity of the sample and reference beams as well as the analog-to-digital converter used in the system. Likewise, the actual pixel size would be determined by probe and system parameters, but could be controlled by the system software.

To compare data sets taken with different imaging systems, or with different probes, we must compensate for these system differences. If the pixel size and intensity ranges are known, as they often are, then it would be possible to use resizing and normalization techniques to account for some of those differences. Furthermore, because the OCT signal strength decreases with depth, the penetration depth and the portion of the image analyzed will play a significant role on the results of any image analysis. This paper will address the effects of system differences on OCT diagnosis algorithms, using two algorithms designed for the recognition of bladder cancer as an example.

2.4.

Wavelets

An approach that could help reduce the effect of system parameters on texture features is the calculation of texture features on the output of wavelet analysis. Wavelet analysis is similar to Fourier analysis but uses waveforms of limited duration and irregular form to describe a signal, instead of infinitely long sine waves. Once the “mother wavelet” is selected, it is shifted to provide temporal information, and scaled to provide information at different scales. At each shifted location, a window of the original signal is compared to the scaled wavelet and a wavelet coefficient is calculated. After this comparison is made across the input signal, a series of wavelet coefficients is available for the given scale. The process can be repeated at any number of scales.³²

As with the discrete Fourier transform, a discrete wavelet transform exists, that limits the wavelet analysis to scales and positions that are powers of two. There can be at most ${\mathit{log}}_{2}N$ stages of analysis if the input signal is $N$ samples long. Each stage, $n$ , of the discrete wavelet transform has two parts. It takes the input of the stage, convolves it with a low-pass filter, and then down-samples the result by 2 to produce what are called the approximation coefficients, $A_n$ . It also takes the input of the stage, convolves it with a high-pass filter, and then downsamples the result by 2 to produce what are called the detail coefficients, $D_n$ . The original signal is the input to the first stage, and the approximation coefficients of the previous stage are the inputs to all subsequent stages.³²

Discrete wavelet analysis can be performed in two dimensions, for analysis of images. On an input image of dimension $m$ by $n$ , the output of the first stage would be an approximation matrix $\mathbf{A}\mathbf{1}$ of dimension $m∕2$ by $n∕2$ , a horizontal detail matrix $\mathbf{H}\mathbf{1}$ of dimension $m∕2$ by $n∕2$ , a vertical detail matrix $\mathbf{V}\mathbf{1}$ of dimension $m∕2$ by $n∕2$ , and a diagonal detail matrix $\mathbf{D}\mathbf{1}$ of dimension $m∕2$ by $n∕2$ . The approximation matrix $\mathbf{A}\mathbf{1}$ would be the input to the second stage. A diagram of the function of the discrete wavelet transform in two dimensions is shown in Figure 1 .³²

Fig. 1

Two-dimensional discrete wavelet transform.

Wavelet analysis is used often for image compression and reconstruction, but can also be used to provide information about an image at several scales.³³ Texture analysis, such as the calculation of co-occurrence features, can be carried out on the approximation or detail coefficients output from various levels of wavelet analysis.³⁴

The application of wavelet analysis to OCT imaging has been predominantly in the area of image denoising^{35, 36, 37} and the results have been impressive. Adler ³⁸ were able to demonstrate an improvement in signal-to-noise ratio of $>7\mathrm{dB}$ using a two-dimensional wavelet filter, while Puvanathasan and Bizheva³⁹ report an improvement of $\sim 10\mathrm{dB}$ in the image signal-to-noise ratio when using a denoising algorithm based on wavelet analysis.

Recently, wavelet analysis has been applied to OCT images for purposes other than image denoising. In 2005, Essock ⁴⁰ studied the possibility of using wavelet analysis on OCT images of the human retina to help diagnose glaucoma. During the study, 134 patients were imaged with OCT and the images analyzed. The second-level approximation coefficients and the Fourier transform of the second-level detail coefficients were normalized and used as image features. Principal components analysis was used to reduce the number of features, and linear discriminant analysis was used as a classifier. The algorithm was very successful, with an area under the receiver operating characteristic (AUC) of 0.947 for the earliest stage of glaucoma, and increasing to 0.997 for the later stages. The AUC is a measure of overall discrimination ability.⁴¹

Although a combination of wavelet and texture analysis has not previously been applied to OCT images for the purpose of detecting cancerous tissue, it has been applied to mammograms by Wei ⁴² for the recognition of cancerous breast tissue. The study used 672 regions of interest extracted from mammograms and calculated co-occurrence features on both the original region and the first four approximation matrices output by the wavelet analysis. When classifying the regions as masses or normal tissue, the method yielded an AUC of 0.86.

When considering system differences, texture analysis on the output of wavelet analysis may have advantages over texture analysis of images directly. The filtering of the data at each step serves to filter out noise, while analyzing the image at courser scales reduces the effect of resolution differences. Furthermore, treating columns and rows separately allows directional properties present in the image to be emphasized or deemphasized in the resulting matrices, which may help differentiate between tissue types.

3.1.

Dataset Description

Both the GWUMC and Baylor studies used OCT imaging systems manufactured by the Imalux Corporation, although the central wavelength of the systems, the signal-to-noise ratios, the range of intensity values, and the pixel sizes were different. During both studies, patients underwent a standard cystoscopic examination. Visually suspect lesions, as well as normal-appearing urothelial tissue, were photographed, scanned with OCT, and biopsied. The scans, which generated $200\times 200–\text{pixel}$ images, were obtained by placing the end-firing OCT probe on the desired site perpendicular to the wall of the bladder. Biopsy specimens were preserved in formalin for standard histopathologic analysis and served as the gold standard for the study.

The GWUMC study included 196 images taken from 22 patients using an OCT imaging system with a $980\text{-}\mathrm{nm}$ central wavelength. Details of the original GWUMC study can be found in Ref. 43. The Baylor study included 96 images taken from 34 patients using an OCT imaging system with a $1310\text{-}\mathrm{nm}$ central wavelength. Because systems with smaller wavelengths have better resolution but less penetration depth, the GWUMC system had better resolution, while the Baylor system had increased penetration. Figure 2 shows example images of healthy, dysplastic, and invasive bladder tissue taken with each system.

Fig. 2

Example images of healthy, dysplastic, and invasive bladder tissue from each data set.

3.2.

Preprocessing

Histogram analysis was used to locate a threshold within each image that indicated the difference between areas having very limited signal and areas representing the bladder. The threshold then was used to separate the portion of the image-containing “background” from the portion to be analyzed. No further preprocessing was done on the images before they were analyzed for training or testing of ALG1.

Prior to being analyzed for training or testing of ALG2, however, the images were normalized, resized, and cropped. The maximum and minimum intensity values for each data set were determined and used to normalize the intensity values over the range of 0–255. The image size is contained in the header of each image and was used to resize images. All images were resized to have a square pixel size of $12\times 12\mu \mathrm{m}$ . The width and depth of each pixel were adjusted to be the same size so that the resulting images would be isotropic. The value of $12\mu \mathrm{m}$ was selected as the width and depth of a pixel because it was larger than the estimated pixel size for all of the images. The largest estimated pixel size was the width associated with some of the Baylor images at $11.5\mu \mathrm{m}∕\text{pixel}$ . Finally, the images were cropped to have a pixel width divisible by eight and to have a height of $96\text{pixels}$ $\left(864\mu \mathrm{m}\right)$ . A depth of $864\mu \mathrm{m}$ was selected to match the system with the least penetration while maintaining a number of pixels that was divisible by eight. The images needed to have pixel dimensions divisible by eight to permit three levels of wavelet analysis.

3.3.

Texture Analysis

Seventy-seven texture features, including Laws’s texture features, histogram features, and co-occurrence features, were calculated for the images. The histogram features were calculated using 8, 32, and 128 bins. The co-occurrence features were calculated using 8, 32, and 128 bins, and using a neighbor defined as one pixel to the right, as well as one pixel down.

In an attempt to further reduce the effect of system differences on ALG2, we extracted additional texture features from the approximation and detail matrices that resulted from three levels of wavelet analysis using the Symlet fourth-order wavelet.⁴⁴

To remove redundant features from the feature sets, we examined each set for correlation between features. Correlation values for each feature pair were calculated and normalized, and one feature removed from consideration if the correlation value had an absolute value of $>0.9$ . Of the 77 features under consideration for ALG1, removing the correlated features left 22. Of the 1001 features under consideration for ALG2, removing the correlated features left 325. To allow comparison between features, the remaining features were normalized over the range of 0–255 before continuing with the analysis.

3.4.

Algorithm

The algorithms were designed to use discriminant analysis to compare the distance between the texture features for each image and the means of the texture features representing the noncancerous and cancerous groups. The image would be declared cancerous if the texture features were closer to the means for the cancerous group, and noncancerous if they were closer to the mean for the noncancerous group. On the basis of prior research indicating that images of normal and dysplasia/carcinoma in situ tissue provide the best separation between classes,¹² normal images were used as representative of the noncancerous group, and dysplasia and carcinoma in situ (CIS) were used to represent the cancerous group. A more complex algorithm using a treelike structure of comparisons or more classes representing different pathologies would require more data to avoid overtraining.

It is known that, for finite sample sizes, there is an optimal number of features; an increase results in performance deterioration.⁴⁵ As more features are used and the algorithm becomes more tuned to the training data set, the algorithm loses its generality or ability to classify a more diverse data set. Consequently, it was necessary to select a subset of the available texture features for use in each of the algorithms.

Prior to feature selection for ALG2, however, system-dependent features were removed from consideration. The trace of the ratio of between-class scatter $S_B$ to within-class scatter $S_W$ (sctrace) was calculated for each feature using the normal images from the GWUMC and Baylor data sets as the two classes. The sctrace is a standard measure of the separation of two classes and is larger for classes that are well separated and lower for classes that overlap. To ensure overlap of the features from the two data sets, only features that had an sctrace value of $<0.2$ were maintained for consideration during feature selection of ALG2. The limit of 0.2 was selected because when plotted it was possible to see differences between system clusters if the sctrace value was $>0.2$ , but not possible when the value was $<0.2$ . Figure 3 shows an example of the distribution of feature values for a set of features with sctrace values $>0.2$ and for a set of features with sctrace values $<0.2$ . Only images with normal pathology were included in the sctrace calculation to remove the effect of greatly varying pathology on the values. It was assumed that images of normal pathology would exhibit well-defined layers, as recognized by Feldchtein,⁴⁶ and have less image-to-image structural variations than varying degrees and types of abnormal pathology.

Fig. 3

Relationship of trace value to system dependence: (a) The features have sctrace values of $>0.2$ , and the feature values for the two systems are separated into two well-defined clusters. (b) The features have sctrace values of $<0.2$ and the feature values for the two systems are intermixed.

For both ALG1 and ALG2, the sctrace of the texture features of the normal and dysplasia/CIS images from the GWUMC data set was used to determine which subset of one, two, or three features would provide the largest separation between the groups of images. The subset with the highest sctrace value was selected for use in the algorithm.

After selection of the features, normal and dysplasia/CIS images from the GWUMC data set were used as training data to determine the mean vectors and covariance matrices for the two classes. Each of the images in the Baylor dataset was then tested using discriminant analysis to classify the image as cancerous or noncancerous.