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Comparative performance analysis of stained histopathology specimens using RGB and multispectral imaging

A performance study was conducted to compare classification accuracy using both multispectral imaging (MSI) and standard bright-field imaging (RGB) to characterize breast tissue microarrays. The study was primarily focused on investigating the classification power of texton features for differentiating cancerous breast TMA discs from normal. The feature extraction algorithm includes two main processes: texton library training and histogram construction. First, two texton libraries were built for multispectral cubes and RGB images respectively, which comprised the training process. Second, texton histograms from each multispectral cube and RGB image were used as testing sets. Finally, within each spectral band, exhaustive feature selection was used to search for the combination of features that yielded the best classification accuracy using the pathologic result as a golden standard. Support vector machine was applied as a classifier using leave-one-out cross-validation. The spectra carrying the greatest discriminatory power were automatically chosen and a majority vote was used to make the final classification. The study included 122 breast TMA discs that showed poor classification power based on simple visualization of RGB images. Use of multispectral cubes showed improved sensitivity and specificity compared to the RGB images (85% sensitivity & 85% specificity for MSI vs. 75% & 65% for RGB). This study demonstrates that use of texton features derived from MSI datasets achieve better classification accuracy than those derived from RGB datasets. This study further shows that MSI provided statistically significant improvements in automated analysis of single-stained bright-field images. Future work will examine MSI performance in assessing multistained specimens.