A multi-spectral imaging system was built from low cost components: LEDs and an area-scan camera, that are all housed within a case and controlled by a tablet computer. The system can capture images of tissue at 14 different wavelengths in <10 seconds. Spectra derived from different lateral positions in the images were then fit to a theoretical model based on GPU Monte Carlo simulations in order to estimate the scattering and absorption properties of the tissue at different layers. To better characterize the system’s ability to measure changes in tissue oxy- and deoxyhemoglobin content, images of the forearm of healthy volunteers were imaged before, during, and after short term ischemia and then reperfusion of the arm, which lowered the amount of oxyhemoglobin in the tissue. To decrease tissue oxygen saturation, blood flow to the arm was restricted for 120 sec using a sphygmomanometer (blood pressure cuff), with pressure levels of 170 mm Hg. Repeated measurements were captured with the arm held in a special mount with an aperture built to fix the tissue in place. Overall, the before, during, and after spectra, where there are notable differences between oxy and deoxy-hemoglobin. The analyses showed a significant decrease in oxygen saturation of the venous plexus layer, with moderate changes in blood content. However, changes in the error function were much more sensitive to blood content than oxygen saturation. These results suggest that changes in oxygen saturation levels can be measured using a low cost setup, although at lower accuracy relative to blood content.
Background: When running large trials, histopathology services are used to assess the state of a tissue. However, in many clinics in low resource settings there are large variations in quality of such services, specifically in biopsy processing and histopathological interpretation/assessment of images. Quality assurance (QA) is needed, but it involves physically mailing slides to a remote clinic. A telemedicine solution can address this challenge. Methods: A novel smartphone adapter for microscopes was developed, consisting of a 3D printed attachment and software integration for the image capture. The attachment is used to couple the eyepiece of a low end microscope to a smartphone (Samsung J530). Image capture was controlled through the EVA System app. The entire system was characterized optically using standard calibration targets. Additionally, images captured on the attachment were compared to the standard method of shipping and scanning slides in a high end slice scanner at a remote clinic. Results: The resolution of the entire system (microscope + phone) with a 40X objective was <1 μm. The system is currently undergoing testing in Nigeria as part of a broader cervical cancer screening study.1 Preliminary testing showed similar image quality between the smartphone-based system and high end scanner. Whole slide imaging requires stitching together images into a mosaic, made possible by a mobile application. Conclusion: The results here show that coupling a low end microscope to a smartphone yields similar results to a transporting slides to a high end microscope. Such an attachment can thus potentially provide a telemedicine solution to researchers in low resource settings.
Cervical cancer is a leading cause of death for women in low resource settings. Visual methods for cervical cancer screening have become more widespread. To improve diagnosis of cervical precancerous lesions, a smartphone-based mobile colposcope was developed that uses auxiliary lens and light source inside a custom-designed case. However, acquiring a sharp image in a clinical setting using the mobile colposcope is tricky. For example, trying to use the phone’s auto-focus functionality struggles with the external lens placed in front of the phone’s internal lens, because translation of the internal lens has a non-trivial effect the image. Moreover, auto-focus algorithm struggles with the high contrast caused by artifacts as patients’ vaginal walls and pubic hair. A more robust algorithm that feeds commands back to the phone’s camera module is needed. Previously, a classifier that measures image sharpness was presented. Implementing a method to correct for an out of focus image requires manipulating the smartphone’s camera control parameters. This can be done either through the phone’s operating system (Camera 2 API) or through the manufacturer’s camera interface (Samsung Camera SDK), as called for from the application. This paper reviews how manipulations in a smartphone app affects image quality. In addition to image sharpness, analyses of brightness and color are also presented. Special apps that sweep through camera conditions were developed. Sample images from both anatomical models and calibration targets are given.
Optical spectral images can be used to estimate the amount of bulk absorbers in tissues, specifically oxy- and deoxyhemoglobin, as well as scattering parameters. Most systems that capture spectral image data are large, heavy, and expensive. This paper presents a full end-to-end analysis of a low-cost reflectance-mode multispectral imaging system operating in the visible and near-infrared spectra. The system consists of 13 LEDs mounted on a printed circuit board, a monochrome machine vision camera, and a tablet computer to control the hardware. The bill of materials for the system is less than $1000. Hardware design and implementation are detailed. Calibration, image capture, and preprocessing are also discussed. In validation experiments, excellent agreement is observed in diffuse reflectance measurements between the spectral camera setup and a spectrometer. To demonstrate that such spectral image data can yield meaningful optical measurements in vivo, the forearms of eight volunteers are imaged in the system. Their data are then analyzed to estimate the tissue optical properties of different skin layers using a Monte Carlo lookup table. In three volunteers, spectral images are captured before and after inducing erythema using a warm wet towel. Across the three subjects, a clear increase in the blood content of the superficial plexus layer was observed as a result of the erythema. Collectively, these findings suggest that a low-cost system can capture accurate spectral data and that clinically meaningful information can be derived from it.
Smartphones are currently used in many medical applications and are more frequently being integrated into medical imaging devices. The regulatory requirements in existence today however, particularly the standardization of smartphone imaging through validation and verification testing, only partially cover imaging characteristics with a smartphone. Specifically, it has been shown that smartphone camera specifications are of sufficient quality for medical imaging, and there are devices which comply with the FDA’s regulatory requirements for a medical device such as a device’s field of view, direction of viewing and optical resolution and optical distortion. However, these regulatory requirements do not call specifically for color testing. Images of the same object using automatic settings or different light sources can show different color composition. Experimental results showing such differences are presented. Under some circumstances, such differences in color composition could potentially lead to incorrect diagnoses. It is therefore critical to control the smartphone camera and illumination parameters properly. This paper examines different smartphone camera settings that affect image quality and color composition. To test and select the correct settings, a test methodology is proposed. It aims at evaluating and testing image color correctness and white balance settings for mobile phones and LED light sources. Emphasis is placed on color consistency and deviation from gray values, specifically by evaluating the ΔC values based on the CIEL*a*b* color space. Results show that such standardization minimizes differences in color composition and thus could reduce the risk of a wrong diagnosis.
Cervical cancer is a leading cause of death for women in low resource settings. In order to better detect cervical dysplasia, a low cost multi-spectral colposcope was developed utilizing low costs LEDs and an area scan camera. The device is capable of both traditional colposcopic imaging and multi-spectral image capture. Following initial bench testing, the device was deployed to a gynecology clinic where it was used to image patients in a colposcopy setting. Both traditional colposcopic images and spectral data from patients were uploaded to a cloud server for remote analysis. Multi-spectral imaging (~30 second capture) took place before any clinical procedure; the standard of care was followed thereafter. If acetic acid was used in the standard of care, a post-acetowhitening colposcopic image was also captured. In analyzing the data, normal and abnormal regions were identified in the colposcopic images by an expert clinician. Spectral data were fit to a theoretical model based on diffusion theory, yielding information on scattering and absorption parameters. Data were grouped according to clinician labeling of the tissue, as well as any additional clinical test results available (Pap, HPV, biopsy). Altogether, N=20 patients were imaged in this study, with 9 of them abnormal. In comparing normal and abnormal regions of interest from patients, substantial differences were measured in blood content, while differences in oxygen saturation parameters were more subtle. These results suggest that optical measurements made using low cost spectral imaging systems can distinguish between normal and pathological tissues.
Multispectral imaging of human tissue is a powerful method that allows for quantify scattering and absorption parameters of the tissue and differentiate tissue types or identify pathology. This method requires imaging at multiple wavelengths and then fitting the measured data to a model based on light transport theory. Earlier, a mobile phone based multi-spectral imaging system was developed to image the uterine cervix from the colposcopy geometry, outside the patient’s body at a distance of 200-300 mm. Such imaging of a distance object has inherent challenges, as bright and homogenous illumination is required. Several solutions addressing this problem were developed, with varied degrees of success. In this paper, several multi-spectral illumination setups were developed and tested for brightness and uniformity. All setups were specifically designed with low cost in mind, utilizing a printed circuit board with surface-mounted LEDs. The three setups include: LEDs illuminating the target directly, LEDs illuminating focused by a 3D printed miniature lens array, and LEDs coupled to a mixing lens and focusing optical system. In order to compare the illumination uniformity and intensity performance two experiments were performed. Test results are presented, and various tradeoffs between the three system configurations are discussed. Test results are presented, and various tradeoffs between the three system configurations are discussed.
Multispectral imaging holds great promise as a non-contact tool for the assessment of tissue composition. Performing multi - spectral imaging on a hand held mobile device would allow to bring this technology and with it knowledge to low resource settings to provide a state of the art classification of tissue health. This modality however produces considerably larger data sets than white light imaging and requires preliminary image analysis for it to be used. The data then needs to be analyzed and logged, while not requiring too much of the system resource or a long computation time and battery use by the end point device. Cloud environments were designed to allow offloading of those problems by allowing end point devices (smartphones) to offload computationally hard tasks. For this end we present a method where the a hand held device based around a smartphone captures a multi - spectral dataset in a movie file format (mp4) and compare it to other image format in size, noise and correctness. We present the cloud configuration used for segmenting images to frames where they can later be used for further analysis.
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