Journal articles: 'Non-invasive images'

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Gupta, Ambuj, Yajur Shridhar, Gayathri Mohan, and Shubham Tyagi. "Image Processing Based Non-Invasive Health Monitoring in Civil Engineering." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1163–68. http://dx.doi.org/10.22214/ijraset.2022.47550.

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Abstract: It has been observed that millions of dollars are being invested on highway/subway tunnel maintenanceand restoration all over the globe. But this cost can be minimized if the detection of cracks will be found as earlieras possible. As the restoration process depends on the type of cracks, it is important to plan the required steps to be taken for repairing the destruction caused as earlier as possible. Initially we need to capture very transparent images of the roads/concrete infrastructures as the detection process will depend on those images. The device usedfor scanning and capturing the images of the concrete infrastructures or roads must be configured for picturing high resolution images. After processing the image acquired, it is easier to extract information about the cracks found. Depending on this information, the images could be classified using some decision-making algorithm. Thisprocedure can be implemented on images acquired by any objects or vehicles carrying image sensing terminal, laser distance sensor, image storage and processing servers, central control system and speed sensor. The accuracydepends on the image’s quality and accurate capture.

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Fatemah Memon, Hijab, Suraiya Hirani, Jaweria Yousfani, Reema Aslam, Sehar Mushtaque, and Iqra Memon. "Enamel Demineralization and Remineralization Detection Using Non-invasive Optical Imaging." Pakistan Journal of Medical and Health Sciences 15, no. 9 (September 30, 2021): 2766–69. http://dx.doi.org/10.53350/pjmhs211592766.

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Introduction: Optical Coherence Tomography (OCT), is one of the most emerging diagnostic imaging technique. It is capable of producing 3D images using optical scattering media. The fast signal acquisition quality has made it a promising tool to detect early in vivo and in vitro lesions. The aim of this study was to reproduce previous demineralization results and to detect remineralization using OCT. Methodology: Bovine enamel discs were used thoroughly in this study. The study was done using the flow cell for detecting demineralization and remineralization following 96 hours demineralization and 192 hours remineralization. A time lapse monitoring was done and the lesions were assessed visually. ImageJ software was used to process the images produced through OCT. The lesion depth and intensity was measured across the images produced which helped in assessing the difference between remineralization and demineralization. Results: OCT B-scan images result in increased backscattering light which is considered the main principle to measure lesion depth and mineral loss. Whereas, in remineralization decreased band of light appeared with reduction in porosity during mineral precipitation. The results for remineralization were diverse and could not be assessed. Conclusion: OCT is favorable technique to detect demineralization and remineralization but it still needs a lot of improvement especially regarding remineralization there are limitations which need to be improved.

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Kale, Shruti, Reema Kharat, Sagarika Kalyankar, Sangita Chaudhari, and Apurva Shinde. "Automated Non-invasive Skin Cancer Detection using Dermoscopic Images." ITM Web of Conferences 40 (2021): 03044. http://dx.doi.org/10.1051/itmconf/20214003044.

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Skin Cancer is resulting from the growth of the harmful tumour of the melanocytes the rates are rising to another level. The medical business is advancing with the innovation of recent technologies; newer tending technology and treatment procedures are being developed. The early detection of skin cancer can help the chance of increase in its growth in other parts of body. In recent years, medical practitioners tend to use non invasive Computer aided system to detect the skin cancers in early phase of its spreading instead of relying on traditional skin biopsy methods. Convolution neural network model is proposed and used for early detection of the cancer, and it type. The proposed model could classify the dermoscopic images into correct type with accuracy 91.2%.

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Agrawal, Neelam, Bikesh Kumar Singh, and Kesari Verma. "Non-invasive technique of diabetes detection using iris images." International Journal of Computational Vision and Robotics 9, no. 4 (2019): 351. http://dx.doi.org/10.1504/ijcvr.2019.10022856.

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Verma, Kesari, Bikesh Kumar Singh, and Neelam Agrawal. "Non-invasive technique of diabetes detection using iris images." International Journal of Computational Vision and Robotics 9, no. 4 (2019): 351. http://dx.doi.org/10.1504/ijcvr.2019.101537.

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Rodriguez-Lozano, Francisco J., Fernando León-García, M. Ruiz de Adana, Jose M. Palomares, and J. Olivares. "Non-Invasive Forehead Segmentation in Thermographic Imaging." Sensors 19, no. 19 (September 22, 2019): 4096. http://dx.doi.org/10.3390/s19194096.

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The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions.

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Li, Ming Jian, Jia Han Guo, Zheng Yu, Lei Yan, and Ning Han. "Non-Invasive Image Processing Method for Detecting Seed Vigor." Applied Mechanics and Materials 397-400 (September 2013): 2134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2134.

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With the development of technology, the agricultural industry has become far more efficient and mechanization. In this paper, image processing method was adopted to detect the changes in seeds temperature, aiming at discovering the laws in seed germination. Oak seeds were selected as samples. The method can be divided into two steps----image segmentation, data extraction and analysis. 32400 images for 90 seeds were captured, and the changing curve of seeds temperature was described based on the images we collected. The result showed that the method was available to capture the changes of seeds temperature during its germination. Even, further research aiming at distinguishing seeds vigor by temperature information, is of great value.

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Kajinami, K., N. Takekoshi, and H. Mabuchi. "Images in cardiology. Non-invasive detection of quadricuspid aortic valve." Heart 78, no. 1 (July 1, 1997): 87. http://dx.doi.org/10.1136/hrt.78.1.87.

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Leech, Michelle, John Gaffney, and Laure Marignol. "Improving non-invasive detection of prostate cancer using diffusion-weighted MRI." Advances in Modern Oncology Research 2, no. 6 (December 13, 2016): 309. http://dx.doi.org/10.18282/amor.v2.i6.152.

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Prostate cancer represents 11% of all cancers in the European Union and 9% of all cancer deaths. Standard MRI for prostate cancer includes high-resolution T2-weighted images, which allow for the assessment of the prostate and the tumour, as tumours arising from the peripheral zone appear dark compared to the expected normally bright peripheral gland tissue. Over the past number of years, the application of diffusion-weighted MRI (DW-MRI) has progressed to include both diagnostic and prognostic roles in oncology. DW-MRI images should be analysed together with the T2-weighted images and the corresponding apparent diffusion coefficient (ADC) maps. We searched PubMed, Cochrane, and Science Direct for relevant journal articles and reviews published up until April 2015 using the search terms “functional MRI” OR “multiparametric MRI” OR “MRSI” AND “diagnosis” AND “prostate cancer” OR “prostate”. The literature indicates that DW-MRI is an important parameter in the identification of prostate cancer. It has the ability to improve sensitivity and specificity, relative to T2-weighted images alone, and has been demonstrated to correlate with tumour grade.

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Mazzeo, Pier Luigi, Christian Libetta, Paolo Spagnolo, and Cosimo Distante. "A Siamese Neural Network for Non-Invasive Baggage Re-Identification." Journal of Imaging 6, no. 11 (November 20, 2020): 126. http://dx.doi.org/10.3390/jimaging6110126.

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Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.

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Bhise, Sanvi Pranav, and Raviraj Havaldar. "Non-Invasive Machine Learning-Based Classification of Bone Health." Traitement du Signal 39, no. 5 (November 30, 2022): 1695–702. http://dx.doi.org/10.18280/ts.390527.

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Osteoporosis is a disease that affects both men and women of all ages but is more commonly seen in women. A measure called Bone Mineral Density (BMD) is often used to raise a warning about the disease. BMD is calculated using a variety of image processing algorithms in both X-ray and dual energy X-ray absorptiometry (DEXA) images. It is a measure of the important T-score, which reflects the degree of osteoporosis. There are many ways to quantify BMD, but DEXA is often regarded as the gold standard. The significance of DEXA images for osteoporosis detection was found in several research. The healthcare system has a serious issue with the lack of osteoporosis education and screening. There is a ton of literature available for diagnosing osteoporosis as well. The numerous methods for detecting osteoporosis will be covered in this review. The problems from the literature analysis, image processing algorithms for detecting osteoporosis, interpretations of the results, and potential recommendations are all included in this work.

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Khan, Huda, Anushka Yadav, Reha Santiago, and Sangita Chaudhari. "Automated Non-invasive Diagnosis of Melanoma Skin Cancer using Dermo-scopic Images." ITM Web of Conferences 32 (2020): 03029. http://dx.doi.org/10.1051/itmconf/20203203029.

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Melanoma skin cancer is one of the deadliest cancers today, the rate of which is rising exponentially. If not detected and treated early, it will most likely spread to other parts of the body. To properly detect melanoma, a skin biopsy is required. This is an invasive technique which is why the need for a diagnosis system that can eradicate the skin biopsy method arises. It is observed that the proposed method is successfully detecting and correctly classifying the malignant and non-malignant skin cancer. Finally, a neural network is used to classify benign and malignant images from the extracted features. Keywords: Melanoma, non-invasive, skin lesion, neural network.

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Fioravanti, Marco, Giuseppina Di Giulio, Giovanni Signorini, Gabriele Rossi Rognoni, Nicola Sodini, Giuliana Tromba, and Franco Zanini. "Non-invasive wood identification of historical musical bows." IAWA Journal 38, no. 3 (November 7, 2017): 285–96. http://dx.doi.org/10.1163/22941932-20170172.

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We identified the wood of the sticks of eight bows in the historical collection of musical instruments in the Galleria dell’ Accademia in Florence. Wood identification was carried out non-invasively (i.e., without sampling wood from the original objects), because the removal of samples from fine musical instruments will affect their aesthetic integrity and/or functional quality. Identification attempts using reflected light microscopy of wood surfaces, gave only partial results due to the poor quality of the surfaces and the particular geometry of the sticks that does not have any transverse surface. Application of Synchrotron light X-ray microtomography (µCT) in phase-contrast mode to the whole sticks allowed us to obtain stacks of transverse-sectional images that, processed as virtual volumes, revealed several anatomical features. With µCT it was possible to identify three bows as Brosimum guianense (Moraceae), one bow as Caesalpinia echinata (Caesalpiniaceae), and four bows as Manilkara sp. (Sapotaceae).

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Singh, Parminder. "Non-invasive Blood Glucose Level Measurement from LASER Reflected Spectral Patterns Images." IOSR Journal of Engineering 03, no. 08 (August 2013): 06–10. http://dx.doi.org/10.9790/3021-03820610.

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ISEKI, YUYA, and TSUGUMI NISHIDATE. "A Deep-Learning Approach for Non-Invasive Temperature Measurements Using Ultrasound Images." Thermal Medicine 37, no. 2 (July 5, 2021): 45–62. http://dx.doi.org/10.3191/thermalmed.37.45.

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Zhang, Jun-Mei, Liang Zhong, Tong Luo, Aileen Mae Lomarda, Yunlong Huo, Jonathan Yap, Soo Teik Lim, et al. "Simplified Models of Non-Invasive Fractional Flow Reserve Based on CT Images." PLOS ONE 11, no. 5 (May 17, 2016): e0153070. http://dx.doi.org/10.1371/journal.pone.0153070.

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Krčmařík, David, Ondřej Novák, and Michal Petrů. "A Novel Method for Non-Invasive Imaging for Medicine and Material Testing." Materials Science Forum 952 (April 2019): 285–94. http://dx.doi.org/10.4028/www.scientific.net/msf.952.285.

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Electrical impedance tomography (EIT) is a perspective vital imaging method in medicine. Although the paper deals mainly with medicine application, the potential of EIT is also in the field of a novel material testing. EIT belongs to non-invasive methods. It is used mainly for the area of thorax since thorax exhibits the biggest changes in impedance. The lungs are mostly filled with air while surrounding flesh has impedance similar to water. However EIT has several drawbacks: relatively big measurement inaccuracy, blurred obtained images, difficulties in application of electrodes and problems with repeatability of measurements. Many drawbacks can be limited when an absolute tomography is used, though absolute tomography is very challenging and is rather only a laboratory concept then a real working method. This article deals with a robust method which is closely related to absolute tomography imaging while viable in clinical practice and to non-invasive material description in general. A unique measuring device VERA was developed. Such a device helps with fast application of electrodes and enables to get reproducible and reliable images of thorax or other volumes in engineering. The paper also discusses challenges when choosing a mediator material or liquid for EIT function. The concept of stiff framework for electrodes which is applied to patient is unusual but exhibits many advantages resulting in better images.

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Quero, Javier, Matthew Burns, Muhammad Razzaq, Chris Nugent, and Macarena Espinilla. "Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks." Proceedings 2, no. 19 (October 24, 2018): 1236. http://dx.doi.org/10.3390/proceedings2191236.

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In this work, we detail a methodology based on Convolutional Neural Networks (CNNs) to detect falls from non-invasive thermal vision sensors. First, we include an agile data collection to label images in order to create a dataset that describes several cases of single and multiple occupancy. These cases include standing inhabitants and target situations with a fallen inhabitant. Second, we provide data augmentation techniques to increase the learning capabilities of the classification and reduce the configuration time. Third, we have defined 3 types of CNN to evaluate the impact that the number of layers and kernel size have on the performance of the methodology. The results show an encouraging performance in single-occupancy contexts, with up to 92 % of accuracy, but a 10 % of reduction in accuracy in multiple-occupancy. The learning capabilities of CNNs have been highlighted due to the complex images obtained from the low-cost device. These images have strong noise as well as uncertain and blurred areas. The results highlight that the CNN based on 3-layers maintains a stable performance, as well as quick learning.

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Tengowski, M. W., L. W. Hedlund, D. J. Guyot, J. E. Burkhardt, and G. A. Johnson. "Non-Invasive Imaging Technologies in Drug Safety Evaluation: Using Magnetic Resonance Imaging in Rats to Assess Theophyline-Induced Testes Toxicity." Microscopy and Microanalysis 7, S2 (August 2001): 626–27. http://dx.doi.org/10.1017/s1431927600029202.

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Non-invasive imaging technologies such as magnetic resonance imaging are commonly used in clinical practice. in an experimental setting, it is possible to decrease field of view and pixel size, thereby increase image resolution. We tested this magnetic resonance microscopy (MRM) technique in a theopln lline-induced rat model of reproductive toxicity. The goal of this work was to study the sensitivity of MRM to detect or predict changes in tissue proton (i.e. water) characteristics, and confirm those findings with histology.Male Sprague-Dawley rats were fed either a control or theophylline (1.3-dimethylxanthine. 8000 ppm) diet. Experimental groups of days 8. 16. 24. and 32 animals were imaged with MRM (Figure 1) and tissue morphology confirmed with histology (Figure 2).Tl-weighted images (with and without gadolinium-DTPA contrast. Magnevist®) and T2-weighted images were acquired at 2 T (spin-echo multislice images. 100 μm2 pixel. 300 μm slice thickness).

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WOLFE, S. W., J. J. CRISCO, and L. D. KATZ. "A Non-Invasive Method for Studying in Vivo Carpal Kinematics." Journal of Hand Surgery 22, no. 2 (April 1997): 147–52. http://dx.doi.org/10.1016/s0266-7681(97)80050-2.

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Seven uninjured and three injured patients were studied using midsagittal computed tomographic (CT) images at 10° increments from full extension to full flexion. Each injured patient had a confirmed scapholunate ligament tear and normal radiographs. CT bony contours were digitized, and incremental motion determined using a specifically designed automated contour-matching algorithm. We expressed wrist motion as a ratio of lunocapitate (midcarpal) motion, and radiolunate (radiocarpal) motion. In normal wrists, motion occurred equally at the midcarpal and radiocarpal joints. In wrists with scapholunate ligament disruption, lunocapitate motion increased significantly throughout the arc of motion.

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Kawattikul, Khanabhorn, Kodchanipa Sermsai, and Phatthanaphong Chomphuwiset. "Improving the sub-image classification of invasive ductal carcinoma in histology images." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 326. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp326-333.

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Whole <span>slide image (WSI) processing is a common technique used in the analysis process performed by pathologists. Identifying precise and accurate regions of cancerous in the tissue is an important process in the disease diagnosis modality. This work proposes an automated technique for identifying invasive ductal carcinoma (IDC) in histology images using. An image is divided into small non-overlapped patches (or image windows). Then, the task is to classify the image patches into different classes, i.e., i) IDC and ii) non-IDC. We employ a two-stage classification-based to classify the patches, as to identify IDC regions in the tissue. In the first stage (patch-level classification), image patch classification is carried out using a conventional handcrafted feature and deep-learning technique are explored. The second stage (post-processing) undergoes a refinement process, which considers the spatial relationships between the neighboring patches. This stage aims to amend some of miss-classified patches. Markov random field (MRF) is implemented in this stage to examine the relationships of the patches and their neighborhoods. The experiments are conducted on public dataset. The experimental results show the post-processing can improve the performance of the classification in the first stage using the handcrafted-based technique and deep learning.</span>

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Saadoon Abdoon, Rabab. "Utilizing image processing techniques for detecting breast abnormalities in thermography images." International Journal of Engineering & Technology 7, no. 4 (October 6, 2018): 2810. http://dx.doi.org/10.14419/ijet.v7i4.18312.

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Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.

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Tindale, W. B., D. C. Barber, H. L. Smart, and D. R. Triger. "Liver Blood Flow: Non-Invasive Estimation Using a Gamma Camera." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 206, no. 2 (June 1992): 99–103. http://dx.doi.org/10.1243/pime_proc_1992_206_273_02.

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A non-invasive radioisotope technique for the measurement of total liver blood flow (TBF) is described. The method requires the use of two intravenously administered tracers, 99mTc (technetium 99m) human serum albumin (HSA) and 99mTc colloid. Computer analysis of first-pass time activity curves for HSA for liver and lung tissues yields values for the arterial and portal contributions to liver blood flow, from which TBF can be determined. These values are then corrected for attenuation using the images of the colloid distribution. The use of the method is illustrated in 17 subjects. Assumptions, limitations and possible applications of the technique are discussed.

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Saratxaga, Cristina L., Aintzane Asumendi, Jesús Gardeazabal, Rosa M. Izu, Ana Sanchez, Goikoana Cancho-Galan, Celia Morales, et al. "Melanoma and Nevi Subtype Histopathological Characterization with Optical Coherence Tomography." Life 13, no. 3 (February 23, 2023): 625. http://dx.doi.org/10.3390/life13030625.

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Background: Melanoma incidence has continued to rise in the latest decades, and the forecast is not optimistic. Non-invasive diagnostic imaging techniques such as optical coherence tomography (OCT) are largely studied; however, there is still no agreement on its use for the diagnosis of melanoma. For dermatologists, the differentiation of non-invasive (junctional nevus, compound nevus, intradermal nevus, and melanoma in-situ) versus invasive (superficial spreading melanoma and nodular melanoma) lesions is the key issue in their daily routine. Methods: This work performs a comparative analysis of OCT images using haematoxylin–eosin (HE) and anatomopathological features identified by a pathologist. Then, optical and textural properties are extracted from OCT images with the aim to identify subtle features that could potentially maximize the usefulness of the imaging technique in the identification of the lesion’s potential invasiveness. Results: Preliminary features reveal differences discriminating melanoma in-situ from superficial spreading melanoma and also between melanoma and nevus subtypes that pose a promising baseline for further research. Conclusions: Answering the final goal of diagnosing non-invasive versus invasive lesions with OCT does not seem feasible in the short term, but the obtained results demonstrate a step forward to achieve this.

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Nisa, Mehrun, Saeed Ahmad Buzdar, Sadia Riaz, Mustansar Mahmood Warraich, and Muhammad Saeed Ahmad. "Non-Invasive Technique to Classify Cirrhotic Liver Using Texture Parameters." July 2021 40, no. 3 (July 1, 2021): 663–70. http://dx.doi.org/10.22581/muet1982.2103.18.

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Texture analysis is an outstanding and fundamental task being used in many medical and computer vision applications. Malfunctioning of the human liver upsets almost all the other organs in the human body. Usually liver infections are difficult to analyze because of inconclusive side effects. More often, the liver could be confronting critically but it may not be significantly unveiled. The main objective of this research work is to provide some standard liver diagnostic measures to minimize the risk factors, as better diagnosis is essential requirement in radiology. The Computerized Tomography (CT) contributes important information to the clinical evaluation of diffuse liver diseases. Haralick texture parameters have been computed on the selected Regions of Interest. B11 is used for discrimination and interpretation of normal and cirrhotic liver diseases. Normal and diseased Liver CT images were collected from Bahawal Victoria Hospital. Normal and cirrhotic liver samples of clinically verified patients were obtained and total 900 Regions of Interest (ROIs) were taken from the selected data. Training of the classes was next step after texture parameter computation. In this work, supervised classification method was used to classify the selected images. In this way, the classes were trained in a supervised manner. The maximum accuracy obtained during this research work was 100%, linear dimensionality was 1 and the linear separability was 0.99%. Results of this research work suggested that texture parameters have high degree of reliability to automatically discriminate similar tissue textures, when regions are obvious. This framework separates benign from malignant liver tumors with moderately high precision and is therefore link up the psychophysics with machine vision to outline, recognize, categorize or discriminate textures.

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Khong, Fan Yi, Tee Connie, Michael Kah Ong Goh, Li Pei Wong, Pin Shen Teh, and Ai Ling Choo. "Non-invasive health prediction from visually observable features." F1000Research 10 (September 13, 2021): 918. http://dx.doi.org/10.12688/f1000research.72894.1.

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Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.

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Khong, Fan Yi, Tee Connie, Michael Kah Ong Goh, Li Pei Wong, Pin Shen Teh, and Ai Ling Choo. "Non-invasive health prediction from visually observable features." F1000Research 10 (March 2, 2022): 918. http://dx.doi.org/10.12688/f1000research.72894.2.

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Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.

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Aldrich, Melissa, Kristen Adams, Bonnie Angermiller, Chinmay Darne, Caroline Fife, Renie Guilliod, Trina Hawkins, et al. "Near-infrared in vivo imaging of human lymphatic architecture and function in lymphedema and normal subjects (65.7)." Journal of Immunology 186, no. 1_Supplement (April 1, 2011): 65.7. http://dx.doi.org/10.4049/jimmunol.186.supp.65.7.

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Abstract Lymphedema (LE) is a disabling condition resulting from genetic predisposition, cancer treatments involving lymph nodes, or nematode infection. Obtaining in vivo images of healthy and diseased lymphatic vessels and lymph nodes, until now, has required using radionucleotide imaging agents, and has resulted in grainy, nondistinct images. We used a custom-designed ICCD camera system and low-level laser excitation of intradermally injected fluorescent dye to obtain live, non-invasive images of lymphatic vessel architecture and function in humans with and without lymphedema. This technique has provided exquisite images and movies of lymphatic abnormalities such as tortuous vessels and patchy “starry night” areas, as well as the ability to measure lymph propulsive velocity and frequency. This non-invasive, live, in vivo imaging of the human lymphatic system in health and disease may provide opportunities to diagnose lymphatic disorders, direct cancer resections, and evaluate therapies.

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Koo, V., P. W. Hamilton, and K. Williamson. "Non-Invasive in vivo Imaging in Small Animal Research." Analytical Cellular Pathology 28, no. 4 (January 1, 2006): 127–39. http://dx.doi.org/10.1155/2006/245619.

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Non-invasive real time in vivo molecular imaging in small animal models has become the essential bridge between in vitro data and their translation into clinical applications. The tremendous development and technological progress, such as tumour modelling, monitoring of tumour growth and detection of metastasis, has facilitated translational drug development. This has added to our knowledge on carcinogenesis. The modalities that are commonly used include Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), bioluminescence imaging, fluorescence imaging and multi-modality imaging systems. The ability to obtain multiple images longitudinally provides reliable information whilst reducing animal numbers. As yet there is no one modality that is ideal for all experimental studies. This review outlines the instrumentation available together with corresponding applications reported in the literature with particular emphasis on cancer research. Advantages and limitations to current imaging technology are discussed and the issues concerning small animal care during imaging are highlighted.

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M. ElMasry, Gamal, and Shigeki Nakauchi. "Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality – A comprehensive review." Biosystems Engineering 142 (February 2016): 53–82. http://dx.doi.org/10.1016/j.biosystemseng.2015.11.009.

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Crawshaw, Jessica R., Marcus J. Kitchen, Corinna Binder-Heschl, Marta Thio, Megan J. Wallace, Lauren T. Kerr, Charles C. Roehr, et al. "Laryngeal closure impedes non-invasive ventilation at birth." Archives of Disease in Childhood - Fetal and Neonatal Edition 103, no. 2 (October 20, 2017): F112—F119. http://dx.doi.org/10.1136/archdischild-2017-312681.

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BackgroundNon-invasive ventilation is sometimes unable to provide the respiratory needs of very premature infants in the delivery room. While airway obstruction is thought to be the main problem, the site of obstruction is unknown. We investigated whether closure of the larynx and epiglottis is a major site of airway obstruction.MethodsWe used phase contrast X-ray imaging to visualise laryngeal function in spontaneously breathing premature rabbits immediately after birth and at approximately 1 hour after birth. Non-invasive respiratory support was applied via a facemask and images were analysed to determine the percentage of the time the glottis and the epiglottis were open.HypothesisImmediately after birth, the larynx is predominantly closed, only opening briefly during a breath, making non-invasive intermittent positive pressure ventilation (iPPV) ineffective, whereas after lung aeration, the larynx is predominantly open allowing non-invasive iPPV to ventilate the lung.ResultsThe larynx and epiglottis were predominantly closed (open 25.5%±1.1% and 17.1%±1.6% of the time, respectively) in pups with unaerated lungs and unstable breathing patterns immediately after birth. In contrast, the larynx and the epiglottis were mostly open (90.5%±1.9% and 72.3%±2.3% of the time, respectively) in pups with aerated lungs and stable breathing patterns irrespective of time after birth.ConclusionLaryngeal closure impedes non-invasive iPPV at birth and may reduce the effectiveness of non-invasive respiratory support in premature infants immediately after birth.

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Ragul, C., and R. Sanmugasundaram. "Non Invasive Method of Diabetes Detection using Particle Swarm Optimization in Tongue Images." International Journal of Business Intelligents 5, no. 1 (June 15, 2016): 35–37. http://dx.doi.org/10.20894/ijbi.105.005.001.009.

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Zavala-De Paz, J., C. Isaza, J. Mosquera-Mosquera, E. Anaya-Rivera, J. Rizzo-Sierra, O. PalilleroSandoval, and J. Escobedo. "Non-invasive Methodology for the Study of Wound Healing Process Using Spectral Images." IEEE Latin America Transactions 18, no. 04 (April 2020): 687–95. http://dx.doi.org/10.1109/tla.2020.9082211.

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Braz, A. K. S., A. S. L. Gomes, T. Y. Ohulchanskyy, and P. N. Prasad. "Failures of adhesion: Non-invasive and three-dimensional images using two-photon microscopy." Dental Materials 26 (January 2010): e3. http://dx.doi.org/10.1016/j.dental.2010.08.013.

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Victorino, Gonçalo Filipe, Ricardo Braga, José Santos-Victor, and Carlos M. Lopes. "Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases." OENO One 54, no. 4 (October 29, 2020): 833–48. http://dx.doi.org/10.20870/oeno-one.2020.54.4.3616.

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Forecasting vineyard yield with accuracy is one of the most important trends of research in viticulture today. Conventional methods for yield forecasting are manual, require a lot of labour and resources and are often destructive. Recently, image-analysis approaches have been explored to address this issue. Many of these approaches encompass cameras deployed on ground platforms that collect images in proximal range, on-the-go. As the platform moves, yield components and other image-based indicators are detected and counted to perform yield estimations. However, in most situations, when image acquisition is done in non-disturbed canopies, a high fraction of yield components is occluded. The present work’s goal is twofold. Firstly, to evaluate yield components’ visibility in natural conditions throughout the grapevine’s phenological stages. Secondly, to explore single bunch images taken in lab conditions to obtain the best visible bunch attributes to use as yield indicators.In three vineyard plots of red (Syrah) and white varieties (Arinto and Encruzado), several canopy 1 m segments were imaged using the robotic platform Vinbot. Images were collected from winter bud stage until harvest and yield components were counted in the images as well as in the field. At pea-sized berries, veraison and full maturation stages, a bunch sample was collected and brought to lab conditions for detailed assessments at a bunch scale.At early stages, all varieties showed good visibility of spurs and shoots, however, the number of shoots was only highly and significantly correlated with the yield for the variety Syrah. Inflorescence and bunch occlusion reached high percentages, above 50 %. In lab conditions, among the several bunch attributes studied, bunch volume and bunch projected area showed the highest correlation coefficients with yield. In field conditions, using non-defoliated vines, the bunch projected area of visible bunches presented high and significant correlation coefficients with yield, regardless of the fruit’s occlusion.Our results show that counting yield components with image analysis in non-defoliated vines may be insufficient for accurate yield estimation. On the other hand, using bunch projected area as a predictor can be the best option to achieve that goal, even with high levels of occlusion.

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Namboodiri, Mahesh, Tahirzeb Khan, Khadga Karki, Mehdi Mohammad Kazemi, Sidhant Bom, Günter Flachenecker, Vinu Namboodiri, and Arnulf Materny. "Nonlinear spectroscopy in the near-field: time resolved spectroscopy and subwavelength resolution non-invasive imaging." Nanophotonics 3, no. 1-2 (April 1, 2014): 61–73. http://dx.doi.org/10.1515/nanoph-2013-0044.

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AbstractThe combination of near-field microscopy along with nonlinear optical spectroscopic techniques is presented here. The scanning near-field imaging technique can be integrated with nonlinear spectroscopic techniques to improve spatial and axial resolution of the images. Additionally, ultrafast dynamics can be probed down to nano-scale dimension. The review shows some examples for this combination, which resulted in an exciton map and vibrational contrast images with sub-wavelength resolution. Results of two-color femtosecond time-resolved pump-probe experiments using scanning near-field optical microscopy (SNOM) on thin films of the organic semiconductor 3,4,9,10 Perylenetetracarboxylic dianhydride (PTCDA) are presented. While nonlinear Raman techniques have been used to obtain highly resolved images in combination with near-field microscopy, the use of femtosecond laser pulses in electronic resonance still constitutes a big challenge. Here, we present our first results on coherent anti-Stokes Raman scattering (fs-CARS) with femtosecond laser pulses detected in the near-field using SNOM. We demonstrate that highly spatially resolved images can be obtained from poly(3-hexylthiophene) (P3HT) nano-structures where the fs-CARS process was in resonance with the P3HT absorption and with characteristic P3HT vibrational modes without destruction of the samples. Sub-diffraction limited lateral resolution is achieved. Especially the height resolution clearly surpasses that obtained with standard microCARS. These results will be the basis for future investigations of mode-selective dynamics in the near-field.

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Prokhorova, Alexandra, Sebastian Ley, and Marko Helbig. "Quantitative Interpretation of UWB Radar Images for Non-Invasive Tissue Temperature Estimation during Hyperthermia." Diagnostics 11, no. 5 (April 30, 2021): 818. http://dx.doi.org/10.3390/diagnostics11050818.

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The knowledge of temperature distribution inside the tissue to be treated is essential for patient safety, workflow and clinical outcomes of thermal therapies. Microwave imaging represents a promising approach for non-invasive tissue temperature monitoring during hyperthermia treatment. In the present paper, a methodology for quantitative non-invasive tissue temperature estimation based on ultra-wideband (UWB) radar imaging in the microwave frequency range is described. The capabilities of the proposed method are demonstrated by experiments with liquid phantoms and three-dimensional (3D) Delay-and-Sum beamforming algorithms. The results of our investigation show that the methodology can be applied for detection and estimation of the temperature induced dielectric properties change.

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YU, JEN-FANG, GO-LONG TSAI, CHUNG-CHIEH FAN, CHING-I. CHEN, CHIA-CHI CHENG, and CHENG-CHUNG CHEN. "NON-INVASIVE TECHNIQUE FOR IN VIVO HUMAN EAR CANAL VOLUME MEASUREMENT." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1250064. http://dx.doi.org/10.1142/s0219519412500649.

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This prospective study is to develop a new non-invasive in vivo technique for the measurement of the human external auditory canal (EAC) volume using high resolution computed tomography (HRCT). Eighteen ears of nine male volunteers, with an age range of 20–30, were measured. The EAC volume was measured using three different methods: tympanometry, water injection, and HRCT. Two dimensional image slices of the EAC, in vivo, were obtained from the raw HRCT data. Each 2D CT image slice was composed of 512 × 512 pixels, with a 0.5 mm slice thickness, and a 0.175 × 0.175 × 0.5 mm3 voxel size. HRCT images were processed with Amira® software (Visage Imaging, Inc., San Diego, USA). Three-dimensional images of the EAC were reconstructed using an unconstrained smoothing mode. The resulting volume of the 3D model of the EAC was calculated using a voxel gate stacked tool. Using tympanometry, the mean measured EAC volume, averaged over all ears, was 766.66 mm3 (Standard Deviation, SD = 194.03 mm3). Using water injection, the mean measured EAC volume was 1102.77mm3 (SD = 121.60 mm3). Using HRCT, the mean measured EAC volume was 1082.22 mm3 (SD = 119.63 mm3). As evident from the SD values, variability was greater in the tympanometry group than in the water injection and HRCT groups. This study successfully developed a new technique to measure the EAC volume using HRCT imaging. This non-invasive technique for in vivo EAC volume measurement was less variable than tympanometry, and avoided the intrusive aspect of measurement by water injection. The technique allows effective, non-invasive assessment of the EAC volume pre-canalplasty, providing a predictor of EAC volume post-canalplasty.

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Mietchen, D., H. Keupp, B. Manz, and F. Volke. "Non-invasive diagnostics in pathological fossils by magnetic resonance imaging." Biogeosciences Discussions 2, no. 2 (March 14, 2005): 239–60. http://dx.doi.org/10.5194/bgd-2-239-2005.

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Abstract. For more than a decade, Magnetic Resonance Imaging (MRI) has been routinely employed in clinical diagnostics because it allows to non-invasively study anatomical structures and physiological processes in vivo and to differentiate between healthy and pathological states, particularly in soft tissue. Here, we demonstrate that MRI can likewise be applied to fossilized biological samples and help in elucidating paleopathological and paleoecological questions: Five anomalous guards of Jurassic and Cretaceous belemnites are presented along with putative paleopathological scenarios directly derived from 3D Magnetic Resonance images with microscopic resolution. These syn vivo deformities of both the mineralized internal rostrum and the surrounding former soft tissue can be traced back in part to traumatic events of predator-prey-interactions, and partly to parasitism. Evidence is presented that the frequently observed anomalous apical collar might be indicative of an inflammatory disease. Finally, the potential of Magnetic Resonance techniques for further paleontological applications is being discussed.

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Huh, Sooin, Hye-Jin Kim, Seungah Lee, Jinwoo Cho, Aera Jang, and Joonsung Bae. "Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness." Sensors 21, no. 3 (February 2, 2021): 1001. http://dx.doi.org/10.3390/s21031001.

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This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.

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Huang, Qin, Wenqi Lv, Zhanping Zhou, Shuting Tan, Xue Lin, Zihao Bo, Rongxin Fu, et al. "Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data." Diagnostics 13, no. 4 (February 9, 2023): 648. http://dx.doi.org/10.3390/diagnostics13040648.

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Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one’s health status, but few studies have revealed that the eye’s features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.

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Khlynov, Ruslan D., Victoria A. Ryzhova, Sergey N. Yarishev, Igor A. Konyakhin, Valery V. Korotaev, Yuri E. Shelepin, Todor S. Djamiykov, and Marin B. Marinov. "Analysis of Polarization Images in the Microphysical Blood Parameters Research for the Hematocrit Diagnostics." Micromachines 13, no. 12 (December 16, 2022): 2241. http://dx.doi.org/10.3390/mi13122241.

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The development of non-invasive optoelectronic technologies for human blood monitoring is one of the important research areas for medicine. A critical analysis of optoelectronic methods of blood research and the micromechanical systems based on them is carried out in this article. A design realization of a polarizing portable system for non-invasive monitoring of hematocrit as one of the basic homeostatic constants of the human body containing information about the microphysical parameters of blood cells has been substantiated. A physical model of polarized radiation conversion in a video information system of laser sensing of a biological research object has been formed. Visual and quantitative differences in the spatial distribution of polarization parameters of the scattered radiation for the states of the body with different hematocrit levels have been revealed. A scheme of a multichannel imaging portable system, based on a smartphone using miniature optical and microelectronic components of information conversion for non-invasive monitoring of microphysical blood parameters, has been created. The system implements the principle of polarimetric blood photometry and a multiparametric analysis of the polarization properties of the laser radiation scattered by blood. The developed portable optoelectronic system, based on a smartphone, can be used for rapid blood diagnostics in disaster medicine and the presence of clinical contraindications to the formation of invasive tests. The proposed polarization-based approach is a promising automated alternative to traditional devices and systems for the research of microphysical blood parameters.

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Tsuda, M., M. Yamaguchi, H. Toyokawa, T. Yabe, Y. Nagao, N. Kawachi, R. Satori, M. Sakai, W. Kada, and T. Kamiya. "Non-invasive range-shift measurement for therapeutic carbon-ion beams by detecting SEB with a CdTe imager." Journal of Instrumentation 18, no. 04 (April 1, 2023): C04008. http://dx.doi.org/10.1088/1748-0221/18/04/c04008.

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Abstract Non-invasive range measurement of particle beams is important to prevent deviation of the irradiated area in particle therapy. In this study, we made an experiment of imaging carbon-ion beams by setting the projection ranges into an acrylic target from 4.29 cm to 7.65 cm by a pitch of 0.21 cm. Secondary electron bremsstrahlung (SEB) generated on the beam trajectories were detected for the imaging by use of a pinhole camera system consisting of a pinhole collimator and a position-sensitive cadmium-telluride semiconductor detector (CdTe imager). Beam images were acquired, and their profiles along the longitudinal axis were numerically analyzed, to obtain a suitable parameter that was strongly correlated with the set range. Then we could propose a scheme to measure the particle beam range in the target during irradiation by use of the CdTe imager. As a result, the range shift can be measured in the case of the acrylic target with an accuracy of 0.1 cm.

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Kato, Kazuo, Yasuhiro Shindo, and Yuya Iseki. "Development of non-contact three-dimensional resonator applicator with non-invasive temperature measurement function." Impact 2021, no. 5 (June 7, 2021): 6–7. http://dx.doi.org/10.21820/23987073.2021.5.6.

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Cancer affects the lives of many people across the world. Indeed, it is the second leading cause of death globally. There are a number of existing cancer treatments but they have limitations. For example, many treatments destroy diseased tissue but also damage healthy tissue in the process. Professor Kazuo Kato, Department of Mechanical Engineering Informatics, School of Science and Technology, Meiji University, Japan, is working to discover how cancer cells can be killed without harming the patient. This work centres on the principles that underlie hyperthermia, which is a cancer treatment that can treat cancer without damaging the human body. It does this by exposing body tissue to high temperatures and cancer cells are more sensitive to heat than normal cells. Kato and the team are developing a hyperthermia system that is able to heat cells in various tissues while using ultrasound images to non-invasively measure the 3D temperature distribution inside the human body. Using this technique, cancerous tissues can be heated and treated non invasively. An additional benefit of the novel ultrasound imaging system and cavity resonator that the researchers are developing is that it is smaller and less expensive than existing treatments.

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Markiewicz, J., K. Górecka, D. Zawieska, M. Zieliński, S. Łapiński, and P. Kot. "THE INTEGRATION OF THE MULTI-TEMPORAL CONSERVATION WORKS AND NON-INVASIVE MEASUREMENTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (February 25, 2022): 343–49. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-343-2022.

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Abstract. Heritage sites are affected by many factors that cause degradation and decay over time, such as soil deformation, changes in weather conditions, or excess moisture, which can cause damages to historical sites. For this reason, restoration works are planned and carried out to preserve the object's existing condition, renovate and repair the damaged parts, or restore the original condition of the object. The Rescue Project was undertaken in the Blessed Ladislas’ chapel in St. Anna's Church in Warsaw, Poland. The main goal of the planned work was to restore the original rococo character of the dome paintings. The aim of this article was to present the integration of the multi-temporal conservation work and non-invasive measurements for changes analysis during conservation works. For this purpose, point clouds acquired with a Z+F 5006h ground-based scanner and images captured via Structure-from-Motion and MultiView Stereo method implemented in Agisoft Metashape software were used. The proposed conservation analysis method was a multi-stage process that involved: (1) automatic data integration using a feature-based approach based on 2D detectors and point clouds converted to spherical images, (2) generalisation and harmonisation of source data, and (3) analysis of deviations between point clouds. Having registered point clouds with each other, it was possible to analyse changes that occurred during conservation work. Calculated distances between both point clouds show the localisation of cracks and damages and enable measure of the size and depth of crevices on the dome wall.

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ATEŞÇİ, Yusuf Z., Utku ŞENTÜRK, Mahmut PEKEDİS, and Murat ÇINAR. "Non-Invasive Urodynamic Analysis Using the Computational Fluid Dynamics Method Based on MR Images." Turkiye Klinikleri Journal of Medical Sciences 31, no. 5 (2011): 1186–93. http://dx.doi.org/10.5336/medsci.2010-21420.

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ISEKI, YUYA, DAISUKE ANAN, TAKAHIRO SAITO, YASUHIRO SHINDO, FUTOSHI IKUTA, KENJI TAKAHASHI, and KAZUO KATO. "Non-Invasive Measurement of Temperature Distributions During Hyperthermia Treatments using Ultrasound B-mode Images." Thermal Medicine 32, no. 4 (2016): 17–30. http://dx.doi.org/10.3191/thermalmed.32.17.

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Nayot, Dan, Natalie Mercuri, Alex Krivoi, Robert F. Casper, James Meriano, and Jullin Fjeldstad. "A NOVEL NON-INVASIVE OOCYTE SCORING SYSTEM USING AI APPLIED TO 2-DIMENSIONAL IMAGES." Fertility and Sterility 116, no. 3 (September 2021): e474. http://dx.doi.org/10.1016/j.fertnstert.2021.08.029.

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Guillon, Jean-Pierre. "IMAGES AND ANALYSIS TO HELP NON INVASIVE TEAR FILM DIAGNOSIS WITH THE TEARSCOPE PLUS." Cornea 19, Supplement 2 (November 2000): S91. http://dx.doi.org/10.1097/00003226-200011002-00065.

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Di Lascio, Nicole, Francesco Stea, Claudia Kusmic, Rosa Sicari, and Francesco Faita. "Non-invasive assessment of pulse wave velocity in mice by means of ultrasound images." Atherosclerosis 237, no. 1 (November 2014): 31–37. http://dx.doi.org/10.1016/j.atherosclerosis.2014.08.033.

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Journal articles on the topic 'Non-invasive images'

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