Example images of haematoxylin and eosin stained normal tissue and tissues with different abnormalities.

<p>(a) Normal tissue, (b) Hyperplastic polyp, (c) Tubular adenoma with low-grade dysplasia and (d) carcinoma. The scale bar shown on (b) is same for all the other sub-images. Note that these images are acquired using a different imaging system and are presented here to illustrate the tissue structure of different classes.</p

Classification accuracy of our method when trained with features extracted from varying number of selected bands.

<p>Various numbers of bands are selected using a method based on similarity based criteria, as described in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197431#pone.0197431.ref007" target="_blank">7</a>]. (a) shows the results of selecting varying number of bands for each texture descriptor separately along with our ensemble approach (b) shows the results obtained for 2-class and 4-class classification. 2-class classification consists of normal and CA class while 4-class classification has all the original classes.</p

Slices/bands of a multispectral image cube of a normal tissue captured using light spectrum of different wavelengths.

<p>First 3 slices with green boundary are captured using visible part of the light spectrum with central wavelength [470 590 710]. Next 3 slices with red boundary are captured using the infrared part of light spectrum with central wavelength [1150 1170 1650]. The scale bar shown on the rightmost slice is same for all the other slices.</p

Colorectal dataset details.

<p>Our experiments were conducted on 200 images taken from 151 patients. For some patients, our dataset comprises more than one biopsy slide.</p

Overall flowchart of our proposed method.

<p>Each feature set is learned by a separate classifier. Weight allocation step represents training/testing a linear SVM classifier using the concatenated probability matrix of all the individual classifiers.</p

Performance comparison between concatenated and ensemble learning approach.

<p>(a) shows classification error (%) of classifiers when trained with various texture features (LBP, LPQ, BSIF, GLCM, Histogram and Perception-like features), concatenated feature set and ensemble learning. These results are presented with both weak and strong cross validation. (b) and (d) show confusion matrix and its corresponding ROC curves for the concatenated features respectively. The mean AUC for CA and TA_LG is 0.908. (c) and (e) show confusion matrix and its corresponding ROC curves for the ensemble learning respectively. The mean AUC for CA and TA_LG is 0.960.</p

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

<div><p>The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.</p></div

Single image showing tissue of two different labels.

<p>The ground truth label given by pathologist for this whole image is HP. While the sub-images on the left side contain normal tissue and sub-images on the right side comprise HP tissue.</p

Results of ensemble learning approach and other comparison algorithms.

<p>LoG, DW and CNN represent Laplacian of Gaussian, discrete wavelet transform and convolutional neural network respectively. In <i>Our Approach 1</i>, parameters fine-tuned on our dataset were used while in <i>Our Approach 2</i>, parameters were fine-tuned for this dataset.</p

Classification accuracy of our method when trained with features extracted from individual bands, all VS bands, all IRS bands and all bands in a multispectral cube.

<p>For both 2-class and 4-class classification, classifier performs better when trained with features extracted from the whole cube in comparison to classifier trained with features from each individual band or all VS bands or all IRS bands. 2-class classification consists of normal and cancerous class only while 4-class classification has all the original classes.</p