Near-infrared imaging and structured light ranging for automatic catheter insertion

Paquit, Vincent; Price, Jeffery R.; Seulin, Ralph; Mériaudeau, Fabrice; Farahi, R. H.; Tobin, Kenneth W.; Ferrell, T. L.

doi:10.1117/12.655326

Cited by 25 publications

(23 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6, it is noticeable that the dark features such as blood vessels and veins are all obviously enhanced without the distortion of the bright areas, on the other hand the images processed by the histogram equalization and WHF are too bright and considerably noisy. The proposed method seems to be very useful in the image enhancement in the fields of the vein authentication [10] and the catheter insertion [11]. The contrast improvements for the original and enhanced features are summarized in Table 1, where the proposed method outperforms the conventional WHF method.…”

Section: Simulationmentioning

confidence: 86%

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Hwang¹,

Oh²

2009

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

In this paper, a new morphology-based homomorphic filtering technique is presented to enhance features in medical images. The homomorphic filtering is performed based on the morphological sub-bands, in which an image is morphologically decomposed. An evolutionary design is carried to find an optimal gain and structuring element of each sub-band. As a search algorithm, Differential Evolution scheme is utilized. Simulations show that the proposed filter improves the contrast of the interest feature in medical images.

show abstract

Section: Simulationmentioning

confidence: 86%

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Hwang¹,

Oh²

2009

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In earlier work, 1 we presented a vision-based system to guide a robot that combined the 2D localization of a vein with a 3D model of the surface of the skin. Using this system, we evaluate the hypothesis that different NIR illuminations can be combined to improve the contrast of venous imagery and that the form of this combination may vary under different physiological characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work, 1 we described benchtop hardware and algorithms to image subcutaneous veins and map the surface topography of the arm or wrist. This system employed NIR illumination for venous imaging and an NIR structured light ranging system for surface mapping.…”

Section: Description Of Purposementioning

confidence: 99%

Combining near-infrared illuminants to optimize venous imaging

Paquit

Price

Mériaudeau

et al. 2007

Medical Imaging 2007: Visualization and Image-Guided Procedures

Self Cite

View full text Add to dashboard Cite

The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. We previously presented work for localizing near-surface veins via near-infrared (NIR) imaging in combination with structured light ranging for surface mapping and robotic guidance. In this paper, we describe experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface. For illumination, we employ an array of NIR LEDs comprising six different wavelength centers from 740nm to 910nm. We capture imagery of each subject under every possible combination of illuminants and determine the optimal combination of wavelengths for a given subject to maximize vein contrast using linear discriminant analysis.Keywords: NIR venous imaging, medical robotics, segmentation and rendering, image-guided therapy DESCRIPTION OF PURPOSEThe ultimate goal of our work is to develop an image-guided robotic system for automated catheterization. In previous work, 1 we described benchtop hardware and algorithms to image subcutaneous veins and map the surface topography of the arm or wrist. This system employed NIR illumination for venous imaging and an NIR structured light ranging system for surface mapping. Our purpose in the work described here is to determine the optimal NIR wavelengths for optimizing vein contrast and to determine if and how those optimal wavelengths vary between subjects with different physiological characteristics (e.g., skin tone and/or hair). METHODS Previous research2 has investigated the propagation of light in tissue as a function of wavelength and, to some extent, the variation in venous image contrast as a function of (monochromatic) illumination wavelength in the NIR range.3 We have constructed a benchtop optical system, as illustrated in Fig. 1(a), that allows us to illuminate an area of the forearm or the hand with any combination of the six different wavelength-centered NIR LEDs. For each subject, we acquire 63 NIR-illuminated images, at video frame rates, corresponding to all possible combinations of the six wavelengths (2 6 − 1). We also acquire an RGB image of the skin surface with a color calibration target to quantify skin tone and visualize any surface hair. For this experiment, apparent veins are identified either manually or automatically from the NIR and/or RGB images. Each vein and background pixel is then represented as a 63-dimensional feature vector corresponding to the (normalized) intensity under each of the 63 illumination conditions. To find the optimal linear combination of illuminants, we perform two-class linear discriminant analysis (LDA) 4 for the vein vs. background problem. Vein segmentation and identification is based on a line detection algorithm developed in remote sensing for r...

show abstract

“…A very important application for structured light is 3D scanning of biological materials, especially human tissue. Examples include head tracking for medical motion correction [22], vision guided surgery [18] [23], medical diagnostics [4][1] [28] and automation in agriculture and farming [21][25] [7]. While the progress in the field has been impressive, one must understand that many target materials are quite problematic.…”

Section: Introductionmentioning

confidence: 99%

An Error Analysis of Structured Light Scanning of Biological Tissue

2017

View full text Add to dashboard Cite

Abstract. This paper presents an error analysis and correction model for four structured light methods applied to three common types of biological tissue; skin, fat and muscle. Despite its many advantages, structured light is based on the assumption of direct reflection at the object surface only. This assumption is violated by most biological material e.g. human skin, which exhibits subsurface light reflection. In this study, we find that in general, structured light scans of biological tissue deviate significantly from the ground truth. We show that a large portion of this error can be predicted with a simple, stochastic linear model based on the scan geometry. As such, scans can be corrected without introducing any specially designed pattern strategy or hardware. We can effectively reduce the error in a structured light scanner applied to biological tissue by as much as factor of two or three.

show abstract

Near-infrared imaging and structured light ranging for automatic catheter insertion

Cited by 25 publications

References 13 publications

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Combining near-infrared illuminants to optimize venous imaging

An Error Analysis of Structured Light Scanning of Biological Tissue

Contact Info

Product

Resources

About