Multispectral Camera System Research Articles

Line-scan system for continuous hand authentication

An increasing number of heavy machinery and vehicles have come into service, giving rise to a significant concern over protecting these high-security systems from misuse. Conventionally, authentication performed merely at the initial login may not be sufficient for detecting intruders throughout the operating session. To address this critical security flaw, a line-scan continuous hand authentication system with the appearance of an operating rod is proposed. Given that the operating rod is occupied throughout the operating period, it can be a possible solution for unobtrusively recording the personal characteristics for continuous monitoring. The ergonomics in the physiological and psychological aspects are fully considered. Under the shape constraints, a highly integrated line-scan sensor, a controller unit, and a gear motor with encoder are utilized. This system is suitable for both the desktop and embedded platforms with a universal serial bus interface. The volume of the proposed system is smaller than 15% of current multispectral area-based camera systems. Based on experiments on a database with 4000 images from 200 volunteers, a competitive equal error rate of 0.1179% is achieved, which is far more accurate than the state-of-the-art continuous authentication systems using other modalities.

An UAS-assisted multi-sensor approach for 3D modeling and reconstruction of cultural heritage site

Abstract Unmanned Aerial System (UAS) has been widely used to produce highly-precise orthomosaics, Digital Surface Models (DSMs), Digital Terrain Models (DTMs) and 3D models in many applications. UAS is also utilized to document cultural heritage sites using low-cost photogrammetric approach. Particularly, possibility of multi-sensor acquisition provides substantial information about both geometric features and material classification. In this study, a novel methodology using multi-sensor data acquisition is proposed in order to extract and to distinguish material features from UAS-based photogrammetry for the cultural heritages. Sensors which are able to collect visible, thermal and infrared radiations of the electromagnetic spectrum were employed to produce 3D model information of Assus Ancient Theater located in Behramkale Village, Canakkale, Turkey. The results showed that the accuracies of the 3D models were obtained as ±2–3 cm, ±10–15 cm and ±5–7 cm for the digital, thermal and multi-spectral camera systems, respectively. Beside the given high-accurate geometric model, the classification outcomes as a result of the spectral analysis revealed material features in an affordable and efficient way.

A UAV–lidar system to map Amazonian rainforest and its ancient landscape transformations

ABSTRACTIn this article, a robust unmanned aerial remote-sensing system, equipped with a survey-grade Lidar scanner and a multispectral camera system, assembled to study pre-Columbian Amazonian archaeology is presented. The data collected from this system will be utilized in a novel inter-disciplinary way by combining these data with in situ data collected by archaeologists, archaebotanists, paleoecologists, soil scientists, and landscape ecologists to study the nature and scale of the impact of pre-Columbian humans in transforming the landscapes of Amazonian rainforest. The outputs of this research will also inform future policy on the conservation, sustainability, and ecological state of the forest.

Development of a high-resolution aerial remote-sensing system for precision agriculture

ABSTRACTAn autonomous unmanned aerial vehicle (UAV)-based aerial remote-sensing system was developed for multispectral imaging of agricultural farms. This system consists of on-board and ground-station subsystems. The on-board subsystem was equipped with body and wings, eight DC brushless motors and eight speed controls, an inertial measurement unit (IMU), a global positioning system (GPS), a rechargeable three-cell lithium-polymer battery, a high-resolution multispectral camera, camera mount, and autopilot intelligent navigation system. The ground station was equipped with a radio control, TV monitor, laptop, and flight monitor software. In addition, a telemetry system was used to communicate between the on-board and ground-station subsystems. To investigate the performance of the UAV, several flight tests were carried out in wheat farms, and some technical features of the system were investigated. The acquired imageries were processed and evaluated. The spatial resolution of the imagery obtained from a height of 10–250 m was 3.6 –95 mm/pixel, respectively. Based on the results, the UAV remote-sensing system proved to be extremely promising for monitoring the temporal changes in the studied farm. The supervised classification map with 94% overall accuracy and kappa coefficient of 0.9 emphasized the conclusions.

VNIR multispectral observations of aqueous alteration materials by the Pancams on the Spirit and Opportunity Mars Exploration Rovers

Multispectral visible and near infrared (VNIR) observations from the Mars Exploration Rover Pancam multispectral stereo camera systems are consistent with materials having been subjected to various aqueous processes. Ferric oxides in the form of hematite in the Burns and Grasberg formations of Meridiani Planum have been well characterized by Opportunity on the basis of strong 535 and 864 nm absorptions and positive 754–1009 nm and 934–1009 nm slopes. On the rim of Noachian-aged Endeavour crater, Opportunity has observed light-toned veins with high Ca and S, as determined by the rover’s Alpha Particle X-ray Spectrometer (APXS), and a negative 934–1009 nm slope in VNIR spectra extracted from Pancam data, indicative of a 1000 nm H2O overtone absorption. Together these observations indicate that the veins are composed of gypsum. Rocks overturned by Opportunity on the Murray Ridge portion of the Endeavour crater rim display dark- and light-toned coatings. The dark-toned coatings have a red, featureless slope that is consistent with the slope observed in laboratory spectra of high-valence manganese oxide minerals. Potential Mn oxide coatings may also be associated with some exposures of the Grasberg formation. APXS results for high Mg and S in the light-toned coatings of the Murray Ridge overturned rocks and a negative 934–1009 nm slope are consistent with hydrated Mg-sulfates. Opportunity has also observed spectral features in rocks that are consistent with orbital observations of Fe-smectites, as well as Al-smectites and possible hydrated silica in light-toned fracture-fill materials. The Spirit rover observed sulfate-rich light-toned soils exposed by the rover’s wheels. Several of these soil observations contained spectral features, such as a broad absorption centered near 800 nm, consistent with ferric sulfate minerals, a finding confirmed by the rover’s Mossbauer spectrometer. Spirit also excavated light-toned Si-rich soils. These soils have a flat near-infrared spectrum with a drop in reflectance from 934–1009 nm that is consistent with free water contained in voids or adsorbed onto the surface of the silica.

A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation

The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based on an industrial multi-sensor camera and a customizable image processing routine. The system was tested in a split fertilized N field trial at different growth stages in between the end of stem elongation and the end of anthesis. The acquired multispectral images were processed to normalized difference vegetation index (NDVI) and red-edge inflection point (REIP) orthoimages for an analysis with simple linear regression models. The best results for the estimation of above-ground biomass were achieved with the NDVI (R 2 = 0.72–0.85, RMSE = 12.3%–17.6%), whereas N content was estimated best with the REIP (R 2 = 0.58–0.89, RMSE = 7.6%–11.7%). Moreover, NDVI and REIP predicted grain yield at a high level of accuracy (R 2 = 0.89–0.94, RMSE = 9.0%–12.1%). Grain protein content could be predicted best with the REIP (R 2 = 0.76–0.86, RMSE = 3.6%–4.7%), with the limitation of prediction inaccuracies for N-deficient canopies.

Image Analysis Approach To Distinguish Lobular Structures In The Mammary Gland From Well-Differentiated Breast Cancer With Tubule Formation

Introduction/ Background Automated detection of diagnostically relevant regions of interest (ROI) is one of the major challenges in medical image analysis. In digital pathology using whole slide images (WSI), the detection of certain biological structures is necessary, because the capacity for analysis of cells and subcellular structures at high resolution is limited by available processing time and computational power. For some applications it is also important to exclude irrelevant or misleading components of the image. One important challenge of this type is well differentiated (low-grade/G1) breast cancer. Aims We aim at automatically distinguishing non-malignant lobular tissue from well-differentiated breast cancer to avoid erroneous evaluation of normal tissue in the detection of nuclear hormone receptor expression. A second goal of lobule detection is to exclude inflammation of non-malignant pre-existing structures from tumor immune cell scoring of breast cancer in oncoimmunology. Methods Two approaches for lobule detection were applied: The first includes modules of own image analysis algorithms developed specifically for lobule detection combined with elements of a commercially available software platform (Definiens Developer®), and the second is a software package optimized for use with a multispectral camera system (Inform, Perkin-Elmer ®). Breast cancer samples were stained for estrogen receptor (ER), progesterone receptor (PR) and the lymphocyte marker CD8. The first approach starts with a texture-based supervised classification to detect lobule candidate regions and uses a nuclear density image to refine the candidate regions. The second approach uses a supervised machine learning method whose features and algorithm are not disclosed to the user. Manual annotations of lobular tissue by expert pathologists were used for evaluation of results. Results The accuracy of distinction between cancer areas and lobular structures decreased in cases with prominent glandular differentiation of the tumor. The major challenge was the separation of well-differentiated (G1) breast cancer with consistent hormone receptor expression from adjacent lobular areas. The second approach performed well on high-grade tumors and had advantages regarding speed and its convenient user interface. The modular first approach was superior on ER and PR images and successfully detected lobular areas even if the anatomical structure was disrupted by inflammation of infiltrating cancer cells. We conclude that modular approaches considering image context and allowing specific adjustments to the tissue of interest may be needed to overcome current limitations of automated ROI detection in clinical biopsy materials.

MDE-based Development of a Multispectral Camera for Precision Agriculture

Abstract: Precision Agriculture in conjunction with Variable Rate Technology aims at increasing productivity while reducing resource usage (water, fertilizers, pesticides and others). Within this context, the farm's management decisions include information from data collected about the crops health development, which need to be gathered in a timely basis. There are several means of obtaining information about the plants health, including manual data collection from sensors installed in the soil and/or its vicinity, mobile sensor systems, for example embedded sensors in UAVs, or aircrafts, or other vehicles, and satellite imagery, to name just a few examples. The adoption of UAVs in such applications has gaining a lot of attention in recent years due to ease of access, low cost and available flight control software. UAV systems may carry several sensors, which include RGB, infrared, multispectral and hyper-spectral cameras. These cameras are respectively more powerful, in terms of data quality, as well as more expensive. The current paper contribution include the platform model, design and characterization of a multispectral camera system made from two low cost, commercial of the shelf cameras that can be integrated in a small UAV. These aforementioned cameras are compatible with open source hardware, namely Raspberry Pi boards, that are used to collect, process and store information.

Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

Operational BRDF Effects Correction for Wide-Field-of-View Optical Scanners (BREFCOR)

The radiometric correction of airborne imagery aims at providing unbiased spectral information about the Earth's surface. Correction steps include system calibration, geometric correction, and the compensation for atmospheric effects. Such preprocessed data are affected by the bidirectional reflectance distribution function (BRDF), which requires an additional compensation step. We present a novel method for a surface-cover-dependent BRDF effects correction (BREFCOR). It uses a continuous index based on bottom-of-atmosphere reflectances to tune the Ross-Thick Li-Sparse BRDF model. This calibrated model is then used to correct for observation-angle-dependent anisotropy. The method shows its benefits specifically for wide-field-of-view airborne systems where BRDF effects strongly affect image quality. Evaluation results are shown for sample data from a multispectral photogrammetric Leica ADS camera system and for HYSPEX imaging spectroscopy data. The scalability of the procedure for various kinds of sensor configurations allows for its operational use as part of standard processing systems.

Multispectral detection of floral buds for automated thinning of pear

Thinning of pome and stone fruit involves the reduction of tree crop load in order to regulate fruit set and quality. As it is typically carried out through manual labor, thinning comprises a large part of a grower’s production costs. Mechanized thinning has been shown to be a cost-effective alternative but the performance of existing thinning devices needs to be further improved by taking the variation in bearing capacity of the individual trees into account.In this work, a multispectral camera system is developed to detect the floral buds of pear (cv. Conference) during the growth stages prior to bloom. During a two-year field trial, the multispectral system was used to measure orchard scenes in six distinct optical wavebands under controlled illumination. These wavebands are situated in the visible and near infrared region of the spectrum and were selected based on hyperspectral laboratory measurements described in previous work.The recorded multispectral images were converted to a database containing the spatial–spectral signatures of the objects present in the orchard. Subsequently, canonical correlation analysis was applied to create a spectral discriminant model that detects pixels originating from floral buds. This model was then applied to the recorded data after which an image analysis algorithm was designed and optimized to predict the number of floral buds. In total, approximately 87% of the visible floral buds were detected correctly with a low false discovery rate (<16%). Therefore, it is expected that the multispectral sensor can be used to improve the efficiency of existing thinning devices. Additionally, it could as well be used as a stand-alone sensor for early-season yield estimation.

Adaptive global training set selection for spectral estimation of printed inks using reflectance modeling

The performance of learning-based spectral estimation is greatly influenced by the set of training samples selected to create the reconstruction model. Training sample selection schemes can be categorized into global and local approaches. Most of the previously proposed global training schemes aim to reduce the number of training samples, or a selection of representative samples, to maintain the generality of the training dataset. This work relates to printed ink reflectance estimation for quality assessment in in-line print inspection. We propose what we believe is a novel global training scheme that models a large population of realistic printable ink reflectances. Based on this dataset, we used a recursive top-down algorithm to reject clusters of training samples that do not enhance the performance of a linear least-square regression (pseudoinverse-based estimation) process. A set of experiments with real camera response data of a 12-channel multispectral camera system illustrate the advantages of this selection scheme over some other state-of-the-art algorithms. For our data, our method of global training sample selection outperforms other methods in terms of estimation quality and, more importantly, can quickly handle large datasets. Furthermore, we show that reflectance modeling is a reasonable, convenient tool to generate large training sets for print inspection applications.

HYPERSPECTRAL REMOTE SENSING WITH THE UAS "STUTTGARTER ADLER" – CHALLENGES, EXPERIENCES AND FIRST RESULTS

Abstract. The UAS "Stuttgarter Adler" was designed as a flexible and cost-effective remote-sensing platform for acquisition of high quality environmental data. Different missions for precision agriculture applications and BRDF-research have been successfully performed with a multispectral camera system and a spectrometer as main payloads. Currently, an imaging spectrometer is integrated in the UAS as a new payload, which enables the recording of hyperspectral data in more than 200 spectral bands in the visible and near infrared spectrum. The recording principle of the hyperspectral instrument is based on a line scanner. Each line is stored as a matrix image with spectral information in one axis and spatial information in the other axis of the image. Besides a detailed specification of the system concept and instrument design, the calibration procedure of the hyperspectral sensor system is discussed and results of the laboratory calibration are presented. The complete processing chain of measurement data is described and first results of measurement-flights over agricultural test sites are presented.

Diagnostic accuracy of diffuse reflectance imaging for early detection of pre-malignant and malignant changes in the oral cavity: a feasibility study

BackgroundDiffusely reflected light is influenced by cytologic and morphologic changes that take place during tissue transformation, such as, nuclear changes, extracellular matrix structure and composition as well as blood flow. Albeit with varying degree of sensitivity and specificity, the properties of diffusely reflected light in discriminating a variety of oral lesions have been demonstrated by our group in multiple studies using point monitoring systems. However, the point monitoring system could not identify the region with the most malignant potential in a single sitting.MethodsIn order to scan the entire lesion, we developed a multi-spectral imaging camera system that records diffuse reflectance (DR) images of the oral lesion at 545 and 575 nm with white light illumination. The diagnostic accuracy of the system for 2-dimensional DR imaging of pre-malignant and malignant changes in the oral cavity was evaluated through a clinical study in 55 patients and 23 healthy volunteers. The DR imaging data were compared with gold standard tissue biopsy and histopathology results.ResultsIn total 106- normal/clinically healthy sites, 20- pre-malignant and 29- malignant (SCC) sites were compared. While the median pixel value of the R545/R575 image ratio for normal/clinically healthy tissue was 0.87 (IQR = 0.82-0.94), they were 1.35 (IQR = 1.13-1.67) and 2.44 (IQR = 1.78-3.80) for pre-malignant and malignant lesions, respectively. Area under the ROC curve to differentiate malignant from normal/clinically healthy [AUC = 0.99 (95% CI: 0.99-1.00)], pre-malignant from normal/clinically healthy [AUC = 0.94 (95% CI: 0.86-1.00)], malignant from pre-malignant [AUC = 0.84 (95% CI: 0.73-0.95)] and pre-malignant and malignant from normal/clinically healthy [AUC = 0.97 (95% CI: 0.94-1.00)] lesions were desirable.ConclusionWe find DR imaging to be very effective as a screening tool in locating the potentially malignant areas of oral lesions with relatively good diagnostic accuracy while comparing it to the gold standard histopathology.

Autofocus for multispectral camera using focus symmetry

A multispectral camera acquires spectral color images with high fidelity by splitting the light spectrum into more than three bands. Because of the shift of focal length with wavelength, the focus of each channel should be mechanically adjusted in order to obtain sharp images. Because progressive adjustment is quite time consuming, the clear focus must be determined by using a limited number of images. This paper exploits the symmetry of focus measure distribution and proposes a simple yet efficient autofocus method. The focus measures are computed using first-order image derivatives, and the focus curve is obtained by spline interpolation. The optimal focus position, which maximizes the symmetry of the focus measure distribution, is then computed according to distance metrics. The effectiveness of the proposed method is validated in the multispectral camera system, and it is also applicable to relevant imaging systems.

인공위성 시스템의 신뢰도 최적 설계에 관한 연구(아리랑위성 2호의 MSC 시스템 구조를 중심으로)

신뢰도란 임의 시스템이 주어진 운용환경 하에서 의도한 기간 동안 의도된 기능을 정상적으로 수행할 확률로 정의된다. 신뢰도-중복 최적화 문제(RROP)는 비용, 무게 등의 제약내에서 시스템의 신뢰도를 최대화할 수 있는 최적의 부품을 선택하고, 부품수와 중복전략(활성/대기중복)을 결정하는 문제이다. 본 연구에서는 아리랑위성 2호의 다채널광학카메라(MSC) 시스템의 설계 구조를 바탕으로 RROP의 수리모형을 제시하고, NP-hard인 RROP의 해법으로써 병렬 개체군집최적화(PPSO) 알고리즘을 제안하였다. RROP 예제의 수치실험 결과는 계획된 수명기간에서 신뢰도를 최대화하는 시스템의 설계 구조를 제시한다. Reliability is defined as a probability that a system will operate properly for a specified period of time under the design operating conditions without failure. Reliability-Redundancy Optimization Problem(RROP) involves selection of components with multiple choices, redundancy levels and redundancy strategy(Active or Standby) for maximizing system reliability with constraints such as cost, weight, etc. Based on the design configuration of Multi-Spectral Camera(MSC) system of KOMPSAT-2, the mathematical programming model for RROP is suggested in this study. Due to the nature of RROP, i.e. NP-hard problem, Parallel Particle Swarm Optimization(PPSO) algorithm is proposed to solve it. The result of the numerical experiment for RROP is presented as instance of recommended design configuration at some mission time.

Detection of head blight ( Fusarium ssp.) in winter wheat by color and multispectral image analyses

The use of camera vision to automatically detect head blight (scab) on wheat ears could provide information about the severity of this dangerous disease and help meet future food traceability requirements. Fusarium spp. is dangerous for both human and animal consumption and the ability to monitor symptom location and severity before the harvested product is further processed or stored could help determine whether the grain is fit for human/animal consumption, for bio-conservation, or is completely unusable. To generate various infection levels, field trials were conducted in 2008 and 2009 using wheat varieties with differing levels of susceptibility to the disease; plots were artificially infected with a spore suspension. A color (red, green, and blue) and a multispectral (red, infrared) camera system with real-time image analysis software were developed and compared to detect disease symptoms in the plots. The chlorophyll defect of the infected wheat ears was classified against the image background by setting binarization thresholds. The result was a black and white image. Single pixels or tiny clusters of pixels not belonging to the symptoms were eliminated by setting an area threshold. For both systems, a linear correlation was found between the camera and the visually detected disease levels of the wheat ears in the plots. In the non-infected control plots without disease symptoms, the multispectral system accurately measured “no disease” even though the digital color system detected too much infection (i.e., a false positive). The multispectral system showed a superior calibration capacity. While the color system had to calibrate for each variety, the multispectral system used only one calibration step before starting the measurements.

Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)

To provide and improved remote sensing a system based on an autonomous UAV was developed. The system was based on an easily transportable helicopter platform weighing less than 14 kg. Equipped with a multi-spectral camera and autonomous system, the UAV system was capable of acquiring multi-spectral images at the desired locations and times. An extended Kalman filter (EKF) based UAV navigation system was designed and implemented using sensor fusion techniques. A ground station was designed to be the interface between a human operator and the UAV to carry out mission planning, flight command activation, and real-time flight monitoring. Based on the navigation data, and the waypoints generated by the ground station, the UAV could be automatically navigated to the desired waypoints and hover around each waypoint to collect field image data. An experiment using the UAV system to monitor turf grass glyphosate application demonstrated the system, which indicated the UAV system provides a flexible and reliable method of sensing agricultural field with high spatial and temporal resolution of image data.

Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology

The prognosis in virtually all solid tumors depends on the presence or absence of lymph node metastases.1-3 Surgical treatment most often combines radical excision of the tumor with a full lymphadenectomy in the drainage area of the tumor. However, removal of lymph nodes is associated with increased morbidity due to infection, wound breakdown and lymphedema.4,5 As an alternative, the sentinel lymph node procedure (SLN) was developed several decades ago to detect the first draining lymph node from the tumor.6 In case of lymphogenic dissemination, the SLN is the first lymph node that is affected (Figure 1). Hence, if the SLN does not contain metastases, downstream lymph nodes will also be free from tumor metastases and need not to be removed. The SLN procedure is part of the treatment for many tumor types, like breast cancer and melanoma, but also for cancer of the vulva and cervix.7 The current standard methodology for SLN-detection is by peritumoral injection of radiocolloid one day prior to surgery, and a colored dye intraoperatively. Disadvantages of the procedure in cervical and vulvar cancer are multiple injections in the genital area, leading to increased psychological distress for the patient, and the use of radioactive colloid.Multispectral fluorescence imaging is an emerging imaging modality that can be applied intraoperatively without the need for injection of radiocolloid. For intraoperative fluorescence imaging, two components are needed: a fluorescent agent and a quantitative optical system for intraoperative imaging. As a fluorophore we have used indocyanine green (ICG). ICG has been used for many decades to assess cardiac function, cerebral perfusion and liver perfusion.8 It is an inert drug with a safe pharmaco-biological profile. When excited at around 750 nm, it emits light in the near-infrared spectrum around 800 nm. A custom-made multispectral fluorescence imaging camera system was used.9.The aim of this video article is to demonstrate the detection of the SLN using intraoperative fluorescence imaging in patients with cervical and vulvar cancer. Fluorescence imaging is used in conjunction with the standard procedure, consisting of radiocolloid and a blue dye. In the future, intraoperative fluorescence imaging might replace the current method and is also easily transferable to other indications like breast cancer and melanoma.

Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology

The prognosis in virtually all solid tumors depends on the presence or absence of lymph node metastases.1-3 Surgical treatment most often combines radical excision of the tumor with a full lymphadenectomy in the drainage area of the tumor. However, removal of lymph nodes is associated with increased morbidity due to infection, wound breakdown and lymphedema.4,5 As an alternative, the sentinel lymph node procedure (SLN) was developed several decades ago to detect the first draining lymph node from the tumor.6 In case of lymphogenic dissemination, the SLN is the first lymph node that is affected (Figure 1). Hence, if the SLN does not contain metastases, downstream lymph nodes will also be free from tumor metastases and need not to be removed. The SLN procedure is part of the treatment for many tumor types, like breast cancer and melanoma, but also for cancer of the vulva and cervix.7 The current standard methodology for SLN-detection is by peritumoral injection of radiocolloid one day prior to surgery, and a colored dye intraoperatively. Disadvantages of the procedure in cervical and vulvar cancer are multiple injections in the genital area, leading to increased psychological distress for the patient, and the use of radioactive colloid. Multispectral fluorescence imaging is an emerging imaging modality that can be applied intraoperatively without the need for injection of radiocolloid. For intraoperative fluorescence imaging, two components are needed: a fluorescent agent and a quantitative optical system for intraoperative imaging. As a fluorophore we have used indocyanine green (ICG). ICG has been used for many decades to assess cardiac function, cerebral perfusion and liver perfusion.8 It is an inert drug with a safe pharmaco-biological profile. When excited at around 750 nm, it emits light in the near-infrared spectrum around 800 nm. A custom-made multispectral fluorescence imaging camera system was used.9. The aim of this video article is to demonstrate the detection of the SLN using intraoperative fluorescence imaging in patients with cervical and vulvar cancer. Fluorescence imaging is used in conjunction with the standard procedure, consisting of radiocolloid and a blue dye. In the future, intraoperative fluorescence imaging might replace the current method and is also easily transferable to other indications like breast cancer and melanoma.