Spectral imaging of BCC with other classifica- classifica-tion methodsclassifica-tion methods

This section compares the performance of several classification methods on Raman spectral imaging with the 2-step LDA method previously developed in this chapter, when compared to the H&E diagnoses.

Several common methods for dimension reduction and classification were applied to the Raman spectral data acquired from two skin tissue samples.

All of these methods start by drastically reducing the dimension of the raw spectral data followed by classification in the space of reduced dimension, which is known as the feature space. The methods were applied to the same set of skin tissue samples. The classification was performed with the func-tion classify of MATLAB changing the different classificafunc-tion algorithms.

In order to train the model 329 mean spectra from 20 patients were used (including 127 BCCs, 92 epidermis, and 110 dermis).

Figures 5.8 and 5.9 are images produced by conventional Raman spectral imaging methods and correspond to MMS tissue sections of known histopa-tholgical diagnosis (see Figure 5.5 (a, d) ) and which will be analysed with the 2-step LDA-based diagnostic method, (see Figure 5.5 (b-c, e-f) ). One of the skin tissue samples contains only dermis and BCC (Figure 5.8) whereas the other tissue section presents also epidermis (Figure 5.9).

For dimensionality reduction PCA was used as an alternative method to spectral band selection. The images were obtained by selecting the 6, 12, and 20 initial PCs of the hyperspectral dataset thus reducing the dimensions of the dataset from 1024. Taking a larger amount of PCs, e.g. 100, increa-sed the noise in the image without revealing any extra information. The final classification was then based on: multiclass linear discriminant analysis (LDA) for 3 classes, diagonal linear discriminant analysis (DLDA), quadra-tic discriminant analysis (QDA), and a type of QDA labelled in MATLAB as “Mahalanobis distance classification”, which does not correspond to the standard Mahalanobis classifier as described in, for example, ref. [181] but whose assumptions are explained in detail in the following paragraphs.

Any dataset can be represented by points in the feature space which are scattered across regions corresponding to the different classes. The regions divide the feature space and their boundaries are the decision boundaries of the classifier. These boundaries are linear for the so-called “linear classifi-cation methods” including LDA and its special case DLDA. An example of a non-linear classification method is QDA which, as it can be inferred from

its name, has a quadratic decision boundary. LDA, DLDA, and QDA are derived by assuming that the features within each class follow a Gaussian distribution. In the case of LDA it is also assumed that all of the classes have a common covariance matrix. The covariance matrix accounts for the pair-wise correlations between the features. The diagonal terms of the covariance matrix are related to the variances of the respective features. If the cova-riance matrix is diagonal the variables/features are uncorrelated. DLDA is a special case of LDA with the extra assumption that the covariance matrix is diagonal [181].

In contrast to LDA and DLDA in QDA a different covariant matrix is as-sociated to each class. The standard “Mahalanobis classifier” as described in ref. [181] assumes all the class covariance matrices to be equal; thus, it is a version of LDA. The difference between LDA and the standard “Ma-halanobis classifier” [181] is that the former sets the decision boundaries to minimise the posterior probability of classification error, taking into account the proportions of the classes in the training set while the latter assumes a priori that the classes are equally probable. This classification method mea-sures the Mahalanobis distance (MD) from each point in the dataset to the centroids (mean features) of the classes and assigns to the unclassified point the class of the centroid for which the MD is smallest. The MD between two vectors is computed as the Eucledian distance between them after they have been de-correlated and normalised to unit variance.

However, the classifier used in the computation performed in this thesis and labelled as “MATLAB Mahalanobis classification” assigns to each class its appropriate class covariace matrix without the assumption of these matrices to be equal, similar to QDA. The main difference with QDA is that the

“MATLAB Mahalanobis classification” assumes that all the classes are a priori equally probable.

Figure 5.8 shows the images produced by selecting the first 6, 12, and 20 PCs of a spectral dataset and classifying them by 3-class LDA. The effect of spectral averaging is also studied. Images without spectral averaging (a-c), and with an average of 4 spectra per pixel (d-f) are presented. The best results are for 6 PCs, either with or without averaging (a) and (d), as they succeed in predicting only 2 classes, in contrast to the 2-step LDA-based classification method. However, when all the classes are present the 2-step LDA-based classification method is more accurate as shown in Figure 5.9.

In Figure 5.9 all the images of each pixel corresponds to the average of 9 spectra. Images with 6, 12 and 20 PCs and linear, diagonal linear, quadratic,

Figure 5.8: Comparison between Raman spectral images produced with unsupervised methods (a-f), the diagnosed histopathological image (g), and the Raman spectral images produced with the supervised 2-step LDA model (h-i). a) 6 PCs, (b) 12, and (c) 20 PCs from spectra with no averaging, classification with linear analysis, (d) 6 PCs, (e) 12 and (f) 20 PCs from image where each pixel is the average of 4 spectra, with linear analysis.

Colour code for (a-f): blue for BCC, green for epidermis, and dark red for dermis. Colour code for (g): dark brown for BCC, lighter brown for dermis. No epidermis present in the sample.

Colour code for (h-i): dark blue for BCC, light blue for epidermis, and yellow for dermis.

and Mahalanobis analysis are presented. The images are less accurate than those produced with the 2-step LDA-based model. The best results were found for (i) quadratic and (l) “MATLAB Malahanobis classification” for 20 PCs, where it was found to be a high BCC detection (the model is more sensitive to the presence of cancer), without misclassifying a large amount of epidermis spectra.

Figure 5.9: Comparison between Raman spectral images produced with unsupervised methods (a-l), the diagnosed histopathological image (m), and the Raman spectral images produced with the supervised 2-step LDA model (n-o).(a) 6 PCs, (b) 12 PCs, and (c) 20 PCs using for classifica-tion linear analysis. (d) 6 PCs, (e) 12 PCs, and (f) 20 PCs using for classificaclassifica-tion diagonal linear analysis. (g) 6 PCs, (h) 12 PCs, and (i) 20 PCS using for classification quadratic analysis. (j) 6 PCs, (k) 12 PCs, and (l) 20 PCs, using for classification the “MATLAB Malahanobis analysis”.

100 PCs gave worse results in terms of diagnostic accuracy (not shown).

Colour code for (a-l): blue for BCC, green for epidermis, and dark red for dermis. Colour code for (g): dark brown for BCC, lighter brown for dermis. Epidermis is also dark brown and it is an horizontal line at the bottom of the image. Colour code for (n-o): dark blue for BCC, light blue for epidermis, and yellow for dermis.

5.4 Summary

In this study it is shown that RMS using supervised classification models can be used for detection and imaging of BCC in skin tissue sections excised during MMS. This technique may represent a feasible alternative towards the development of automated tumour imaging during surgery.

The 2-step LDA-model was developed using 329 Raman spectra from 20 patients, including 127 BCCs, 92 epidermis, and 110 dermis. Selected Raman bands corresponding to nucleic acids and collagen type I were computed and used as input into the multivariate model. BCC was discriminated from healthy tissue with 90±9% sensitivity and 85±9% specificity in a 70%-30%

split cross-validation algorithm.

The model was utilised to build 2D biochemical images of unknown skin tissue samples excised during MMS. The images were obtained using two supervised methods. The first method applied k -means clustering to the whole spectral database, and then ratios of selected Raman bands for each centroid were computed and introduced into the 2-step LDA-based classifi-cation model. The second method directly applied the 2-step LDA model to compute the peak ratios of the whole spectral database. Both analysis me-thods provided quantitative images by applying a classification model on new tissue samples. The pseudo-colour images revealed the presence/absence of tumour without intervention of histopathologists and determined accurately its location within the sample.

The performance of the 2-step LDA-based classification methods was compa-red with other common methods, using principal component analysis for di-mensional reduction in combination with different classification algorithms:

3-class LDA, quadratic discriminant analysis, a special case of quadratic dis-criminant analysis named in this thesis as “ MATLAB mahalanobis analy-sis”, and diagonal linear analysis. Images with different number of principal components (PCs) were tested: 6, 12, and 20. Larger numbers of PCs were found to produce lower agreement (results not shown). These alternative methods were applied to two samples of nodular BCC already analysed by the 2-step LDA-based classification method, from which the H&E diagnosis was known. First, an image of nodular BCC presenting only dermis and cancer was studied, fixing the classification algorithm to 3-class LDA, and varying the number of PCs used for dimensional reduction. The best diagno-sis in terms of visual agreement with the H&E evaluated section was given by choosing only the first 6 PCs, either with our without spectral

avera-ging. This image did not falsely predict the presence of epidermis in the tissue section in contrast to the step LDA-based model. However, the 2-step LDA-based classification methods were found superior when the three classes of skin components were present in the tissue sections, for nodular BCC. Images with 6, 12, and 20 PCs were produced varying the classification algorithm (3 class-LDA, diagonal linear, quadratic, and “MATLAB mahala-nobis analysis”). The diagnostic images were found less accurate than those produced by the 2-step LDA-based supervised technique. The best results from the alternative study in terms of visual agreement to the H&E diag-nosis were found for quadratic and “MATLAB mahalanobis analysis” with 20 PCs, where BCC detection was high although lower than that achieved by the 2-step LDA-classification method.

This study demonstrated the ability of RMS for automated diagnosis of nodular and morphoeic BCC; therefore, the first hypothesis of this thesis is verified: Automated spectral imaging of BCC using RS in combination with mathematical models can be introduced as an alternative to histopathological evaluation of MMS-excised tissue sections. Further studies are required to establish the ability of this technique to detect more types of BCC.

It is also important to consider that the samples used in this study are samples with straightforward diagnosis, i.e. there was no subjectivity in the diagnosis for a trained histopathologist. This was done in order to assess correctly the performance of RMS, as the histopathological diagnosis was taken as the true diagnosis and compared with the automated diagnosis. In this case the technique does not present any real advantage.

The real potential of this technique lies in the automated diagnosis of samples with difficult diagnosis. Further studies in this direction in order to create a model which allows the distinction of BCC from other skin structures which are morphologically similar to BCC, such as hair follicles or inflammation is of great scientific interest. In addition, the potential of RMS to image BCC regions in tissue blocks has still to be demonstrated.

In conclusion, Chapter 5 demonstrates that it is possible to detect and image BCC using RMS. However, there is a weak point in this approach in terms of practical application in a medical theatre: the time required for data acquisition is too long (hours).