Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Abstract

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440. 5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

“…[ 40 ]. GA has been used in several types of optimization problems due to its straightforwardness and robustness [ 41 , 42 , 43 ]. The GA implementation used in the experiments performed in this work was based on the MATLAB ® Global Optimization ToolBox TM .…”

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

“…Additionally, mRMR has been used for the identification of the most relevant bands for ex-vivo breast cancer detection [61], and for in-vivo head and neck cancer [63]. Finally, Martinez-Vega et al proposed a search-based method based on different optimization algorithms for the identification of the most relevant wavelengths for brain tumor detection within in-vivo HS images [64]. The optimization function was the pixel-wise classification performance metrics obtained by an SVM classifier.…”

Demarcation Line Assessment in Anatomical Liver Resection: An Overview

“…It is also possible to quantify the superficial tissue oxygen saturation (StO2) at a depth of ∼1 mm and the oxygenated hemoglobin quantification within the NIR spectrum at a depth of 4-6 mm. 45 HSI has been evaluated in several medical applications, 46 including cancer tissue discrimination, 47,48 gastrointestinal anastomosis perfusion, [49][50][51] and anatomical structures identification. 52,53 However, the lack of a video rate with current devices prevents a smooth application as a surgical navigation tool due to the static side-by-side image display.…”