Direct curvature correction for noncontact imaging modalities applied to multispectral imaging

Abstract

Noncontact optical imaging of curved objects can result in strong artifacts due to the object's shape, leading to curvature biased intensity distributions. This artifact can mask variations due to the object's optical properties, and makes reconstruction of optical/physiological properties difficult. In this work we demonstrate a curvature correction method that removes this artifact and recovers the underlying data, without the necessity of measuring the object's shape. This method is applicable to many optical imaging modalities that suffer from shape-based intensity biases. By separating the spatially varying data (e.g., physiological changes) from the background signal (dc component), we show that the curvature can be extracted by either averaging or fitting the rows and columns of the images. Numerical simulations show that our method is equivalent to directly removing the curvature, when the object's shape is known, and accurately recovers the underlying data. Experiments on phantoms validate the numerical results and show that for a given image with 16.5% error due to curvature, the method reduces that error to 1.2%. Finally, diffuse multispectral images are acquired on forearms in vivo. We demonstrate the enhancement in image quality on intensity images, and consequently on reconstruction results of blood volume and oxygenation distributions.

Characterizing optical properties and spatial heterogeneity of human ovarian tissue using spatial frequency domain imaging

“…26 The ultimate goal is to provide a wide-field, real-time examination of the ovary as well as the fallopian tube for assisting surgeons during minimally invasive surgery and reducing unnecessary surgical procedures. Future work will focus on multispectral characterization of the functional heterogeneity of the ovarian tissue and miniaturization of the device for in vivo examination of the ovary during minimally invasive surgery.…”

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

“…1,9 Although scattering effects are fully considered in these methods, the methods are susceptible to shading-derived errors caused by the incomplete estimation of the irradiance. 8,11,12 This is a serious problem for visual understanding and quantitative evaluation of the chromophore imaging.…”

“…Neither the nonlinearity between the concentration and the absorbance nor the cross-talk between the chromophores has been taken into account. 1,14,15 In order to fully account for the effect of scattering in the estimation of chromophore concentrations, several methods have been considered; these include methods based on random work theory, 12 Kubelka-Munk theory, 8 a support vector machine, 11 and an empirical method aided by a Monte Carlo simulation (MCS). 1,9 Although scattering effects are fully considered in these methods, the methods are susceptible to shading-derived errors caused by the incomplete estimation of the irradiance.…”

“…Therefore, many studies have been conducted to evaluate the concentration of chromophores, [1][2][3][4][5][6] and some of these studies discuss imaging, in which visible light spectroscopy is usually used to achieve high spatial resolution. [7][8][9][10][11][12][13] Multiple regression analysis (MRA) based on the Beer-Lambert (B-L) 3 law is a simple way to correlate the chromophore concentrations with the reflectance of skin. The B-L law has been applied to the three-layer skin model of epidermis, dermis, and fat, with the assumptions that no scattering occurs in the epidermis or dermis and no wavelength dependency exists in the reflectance from the fat layer.…”

“…10 Several studies have attempted to correct for the shading effect. In these studies, the shape of each subject was measured 13,16 or the curvature effect was otherwise extracted 12 and taken into account. However, these approaches require hardware modifications and additional measurements or else assumptions about the shapes of the samples.…”