Abstract
Diffuse reflectance spectroscopy (DRS) has been extensively applied for the characterization of biological tissue, especially for dysplasia and cancer detection, by determination of the tissue optical properties. A major challenge in performing routine clinical diagnosis lies in the extraction of the relevant parameters, especially at high absorption levels typically observed in cancerous tissue. Here, we present a new least-squares support vector machine (LS-SVM) based regression algorithm for rapid and accurate determination of the absorption and scattering properties. Using physical tissue models, we demonstrate that the proposed method can be implemented more than two orders of magnitude faster than the state-of-the-art approaches while providing better prediction accuracy. Our results show that the proposed regression method has great potential for clinical applications including in tissue scanners for cancer margin assessment, where rapid quantification of optical properties is critical to the performance.
Highlights
Noninvasive optical techniques, such as diffuse reflectance spectroscopy (DRS), have been extensively researched for quantitative tissue characterization and disease diagnosis [1,2,3,4,5,6]
We propose an alternate optical property determination approach based on a non-linear multivariate calibration (MVC) framework
Our results suggest that the dual advantage of speed and accuracy of the least-squares support vector machine (LS-SVM) approach makes it ideally suited for application in tissue imaging platforms
Summary
Noninvasive optical techniques, such as diffuse reflectance spectroscopy (DRS), have been extensively researched for quantitative tissue characterization and disease diagnosis [1,2,3,4,5,6]. DRS provides an assessment of scattering of sample (which is primarily a function of density and scattering cross sections of internal structures) as well as absorber composition (hemoglobin and beta-carotene) This wealth of information has led to several applications of DRS including microcirculation monitoring [7,8], pre-cancer and cancer detection [9] and intra-operative tumor margin assessment [10]. The LUT approach involves iterative fitting of the spectra using a non-linear optimization routine, which is computationally expensive (the typical fit time is of the order of a few seconds) This is problematic when the algorithm is deployed in a spectral imaging platform [10], where data may need to be routinely acquired and processed from a few thousand points. The speed of the prediction algorithm is critical to the success of imaging platform applications including the investigation of full tumor margins in intra-operative cancer assessment
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