Context-free hyperspectral image enhancement for wide-field optical biomarker visualization

Abstract

Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed affinity-based color enhancement, or ACE for short, achieves multi-and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.

Affinity-based color enhancement methods for contrast enhancement in hyperspectral and multimodal imaging

“…In this contribution, some issues regarding current hyperspectral enhancement methods have been discussed. The proposition of a novel method [4] that provides identical results with consistent output shows promise in terms of applicability to clinically useful scenarios such as vein delineation, chromophore concentration detection, and surgical margin assessment of lumpectomies.…”

“…This data can be used for training, obtaining a probability map for veins in the rest of the image (center). This vein map shall remain constant regardless of the properties of the background and/or hand orientation, as long as the scatter signatures of veins and skin remain constant, which is not the case for SVD-based traditional methods [4].…”

“…Essentially, the m-rank approximation of the spectrum is subtracted to the original data so that the differences that deviate from the most frequent spectra at specific wavelengths can be exaggerated. This limits the method to only consider statistical anomalies, which are largely dependent on the number of similar or dissimilar spectra present on a given image or, as can be demonstrated, on background elements not pertaining to the specimen of interest [4].…”

“…A specific concentration ratio can be targeted with an expression such as a i = σ (− log 10 (h 2 (c i − c ob j ) 2 )), with c obj the objective concentration, h > 0 a desired bandwidth and σ (.) the sigmoid function [4].…”

“…Then, a colorimetric method with tunable enhancement gain, contrast, and threshold is provided, essentially mimicking the previous methods but without this sensitivity to nonessential information in the image. Its resilience and robustness may prove its potential applicability in the field of guided surgery [4].…”