Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

“…We notice an overestimation of the detected tumor area, that can be due to the number of superpixels or pixel blocks containing both tissues, classified as tumor, as described in ref. 6 As already mentioned in the reference publication, the problem can be overcome by increasing the number of superpixels (which is 1000 in our algorithm) or pixel blocks. Table 1.…”

“…The presented method offers a superior sensitivity and a significant decrease in computation time, when compared with an existing approach. 6 Furthermore, our validation was more relevant for patients than in the original publication, where the benchmark approach was tested on 11 cancer mouse models, whereas we perform our validation on 7 real tongue-cancer patients. To the best of our knowledge, the tongue-tumor location is not completely explored in ex-vivo studies with machine learning techniques combined with HSI, although it is the most aggressive of all oral squamous carcinoma (OSCC) with higher rate of occult lymph node metastases.…”

“…We manually obtain a mask for the region of interest of the tumor area, discarding the image borders affected by histological colorations and motion artifacts due to the line scanning. As an alternative to the manual selection, we apply the implemented method of Chung et al 6 This method employs Principal Component Analysis (PCA) 10 for automated band extraction, iterative clustering and superpixel segmentation of the first components, using averaged pixel values within each superpixel in order to build discriminative feature vectors. The superpixel algorithm is based on the Simple Linear Iterative Clustering (SLIC) technique, 11 which offers high segmentation performance.…”

“…Furthermore, the superpixel segmentation has the advantage of pixel clustering in meaningful regions based on statistical decorrelation of spectral image bands. 6…”

“…We propose two specific aspects: (1) a manual selection of the most informative image bands in a spectral range based on increased discriminative properties in the reflectance spectrum of the tumor and healthy tissue, (2) a block-based feature extraction approach for automated classification purposes. Besides the proposed scheme, we compare this work with an alternative and existing framework that was already applied on mouse cancer models, 6 and evaluated in our study on real patients.…”