Histopathological image analysis for centroblasts classification through dimensionality reduction approaches

Abstract

We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non-CB) cells in digital images of H&E-stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non-CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high-power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non-CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22% ± 0.75% and 99.07% ± 1.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB/non-CB in comparison with the state of the art methods.

Keywords: LDA; Laplacian Eigenmaps; SVD; dimensionality reduction; follicular lymphoma; intrinsic dimensionality.

Figure 1

Images of a CB cell (left image) and Non-CB cells (right image). The scanner’s resolution at 40X magnification is 0.25 μm/pixel, therefore the yellow lines indicate a physical length of 4 μm in the tissue.

Figure 2

RGB image of a CB cell (top left image), its intensity representation in gray scale (top right image), the gray scale image after applying standardization (bottom left image) and the filtered with median filter the same gray scale standardized image (bottom right).

Figure 3

Grayscale version of the first three singular images in the “image space of CB” (top three images) and the first three singular images in the “image space of Non-CB.” (bottom three images).

Figure 4

The cell images (CB image cells shown in a hue of blue, whereas Non-CB image cells shown in a hue of red) that lie on the three dimensional manifold (it is consisted of the three first eigenvectors) and the tested image (shown in black) which resides on the top left of the manifold. This specific tested image cell would be classified as a CB image cell since it resides closer to CB image cells.

Figure 5

Flow chart of the proposed scheme for classification of tested image cells into CB and Non-CB classes. The classification method used was either COB or CLEM, both extracting discriminatory features used in the classification of the tested image cells.

Figure 6

Results of CB precision (top), CB recall (second from top), Non-CB precision (third from top), Non-CB recall (fourth from top), as well as the overall accuracy results (bottom) relative to the number of eigenvectors (or singular images in case of COB) used in the four supervised classification methods that use dimensionality reduction. Here we plot the results of only the first 15 eigenvector, since the rest do not add any discriminative information of the data. Results of methods that use linear dimensionality reduction (CLPLRDS, CLDA and COB) are shown in green, red, and blue respectively, whereas for CLEM (non-linear dimenionality reduction) results are in black.

Figure 7

(a) Images of a CB cell created based on different points inside its body. The middle image is the image created based on the pathologist’s marking. (b) Image of a cell used on the examination of COB and CLEM’s consistency. New images of the cell were created based on pixels that lie on the colorful rectangulars shown in the figure (created based on a distance d from the cell’s center, shown with a white arrow).

Figure 8

Average results in consistency of COB (blue) and CLEM (blck) for 40 CB cells (top) and 40 Non-CB (middle). Results in consistency are nothing more than the classification accuracy of the new images of the cells, created based on pixels that lie radius pixels away from the original center of the cell (0 point in x-axis). The bars represent the average results and their range represents the standard deviation from this mean value.

Figure 9

Results of classification’s accuracy in COB (blue) and CLEM (black) for 40 CB (top plot) and Non-CB (bottom plot), when taking into account the results of the cell’s images created based on the close to the cells’ center pixels in various radius away from it.