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Journal articles on the topic 'Quantitative imaging analysis'

To see the other types of publications on this topic, follow the link: Quantitative imaging analysis.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Asinovski, L., D. Beaglehole, and M. T. Clarkson. "Imaging ellipsometry: quantitative analysis." physica status solidi (a) 205, no. 4 (April 2008): 764–71. http://dx.doi.org/10.1002/pssa.200777855.

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2

Woo, D.-C., C.-B. Choi, J.-W. Nam, K.-N. Ryu, G.-H. Jahng, S.-H. Lee, D.-W. Lee, et al. "Quantitative analysis of hydrocephalic ventricular alterations in Yorkshire terriers using magnetic resonance imaging." Veterinární Medicína 55, No. 3 (April 15, 2010): 125–32. http://dx.doi.org/10.17221/127/2009-vetmed.

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The purpose of this work was to evaluate hydrocephalic ventricular changes using three quantitative analysis methods. The height, area and volume of the ventricles and brain were measured in 20 Yorkshire terriers (10 normal and 10 hydrocephalic dogs) using low-field MR imaging (at 0.2 Tesla). All measurements were averaged and the relative ventricle size was defined as a percentage (percent size of the ventricle/size of the brain). The difference between normal and hydrocephalic dogs was statistically significant for the average of each ventricle as well as for the percentage value. Five hydrocephalic symptoms were identified: circling, head tilting, seizures, ataxia, and strabismus. With respect to height, area and volume of the brain/ventricle, the difference between normal and hydrocephalic dogs was not significant. The ventricle/brain with height (1D) was related to the area (2D) and volume (3D). The correlations with area and volume were as good as the ventricle/brain height ratio in the case of hydrocephalic dogs. Therefore, one-, two- and three-dimensional quantitative methods may be complementary. We expect that the stage of hydrocephalic symptoms can be classified if statistical significance for ventricular size among symptoms is determined with the analysis of a large number of hydrocephalic cases.

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3

Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (March 1, 1995): 127–35. http://dx.doi.org/10.3109/02841859509173364.

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4

Zaidi, H., and W. D. Erwin. "Quantitative Analysis in Nuclear Medicine Imaging." Journal of Nuclear Medicine 48, no. 8 (August 1, 2007): 1401. http://dx.doi.org/10.2967/jnumed.107.042598.

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5

Harpen, M. D., J. Powell Williams, and J. P. Williams. "Quantitative analysis of NMR spectroscopic imaging." Physics in Medicine and Biology 32, no. 4 (April 1, 1987): 421–30. http://dx.doi.org/10.1088/0031-9155/32/4/001.

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6

Ranade, Vasant. "Quantitative Analysis in Nuclear Medicine Imaging." American Journal of Therapeutics 13, no. 4 (July 2006): 385. http://dx.doi.org/10.1097/00045391-200607000-00018.

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7

Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (January 1995): 127–35. http://dx.doi.org/10.1080/02841859509173364.

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8

Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (March 1995): 127–35. http://dx.doi.org/10.1177/028418519503600204.

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The diagnostic potential of texture analysis in quantitative tissue characterisation by MR imaging at 1.5 T was evaluated in the brain of 6 healthy volunteers and in 88 patients with intracranial tumours. Texture images were computed from calculated T1 and T2 parameter images by applying groups of common first-order and second-order grey level statistics. Tissue differentiation in the images was estimated by the presence or absence of significant differences between tissue types. A fine discrimination was obtained between white matter, cortical grey matter, and cerebrospinal fluid in the normal brain, and white matter was readily separated from the tumour lesions. Moreover, separation of solid tumour tissue and peritumoural oedema was suggested for some tumour types. Mutual comparison of all tumour types revealed extensive differences, and even specific tumour differentiation turned out to be successful in some cases of clinical importance. However, no discrimination between benign and malignant tumour growth was possible. Much texture information seems to be contained in MR images, which may prove useful for classification and image segmentation.

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9

Madsen, Mark. "Quantitative Analysis in Nuclear Medicine Imaging." Medical Physics 34, no. 4 (March 28, 2007): 1522. http://dx.doi.org/10.1118/1.2716416.

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10

Lammertsma, A. "Radioligand studies: imaging and quantitative analysis." European Neuropsychopharmacology 12, no. 6 (December 2002): 513–16. http://dx.doi.org/10.1016/s0924-977x(02)00100-1.

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11

Fleming, John. "Quantitative Analysis in Nuclear Medicine Imaging." Physics in Medicine and Biology 52, no. 11 (April 17, 2007): 3307–8. http://dx.doi.org/10.1088/0031-9155/52/11/b01.

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12

Swedlow, J. R. "Informatics and Quantitative Analysis in Biological Imaging." Science 300, no. 5616 (April 4, 2003): 100–102. http://dx.doi.org/10.1126/science.1082602.

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13

Mayer, J., U. Eigenthaler, J. M. Plitzko, and F. Dettenwanger. "Quantitative analysis of electron spectroscopic imaging series." Micron 28, no. 5 (October 1997): 361–70. http://dx.doi.org/10.1016/s0968-4328(97)00037-1.

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14

Chen, Weiyang, and Jing-Dong J. Han. "Aging phenomics enabled by quantitative imaging analysis." Oncotarget 6, no. 19 (June 22, 2015): 16794–95. http://dx.doi.org/10.18632/oncotarget.4566.

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15

Lee, Jongmin. "Quantitative Analysis in Cardiovascular Imaging: Current Status." Current Medical Imaging Reviews 9, no. 3 (November 2013): 214–22. http://dx.doi.org/10.2174/15734056113096660011.

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16

Dunnwald, L. K., D. A. Mankoff, and S. D. Hartnett. "SEMI-QUANTITATIVE ANALYSIS IN SESTAMIBI BREAST IMAGING." Clinical Nuclear Medicine 20, no. 9 (September 1995): 859. http://dx.doi.org/10.1097/00003072-199509000-00052.

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17

González-Rodríguez, Pedro, Arnold D. Kim, and Chrysoula Tsogka. "Quantitative signal subspace imaging." Inverse Problems 37, no. 12 (November 16, 2021): 125006. http://dx.doi.org/10.1088/1361-6420/ac349b.

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Abstract We develop and analyze a quantitative signal subspace imaging method for single-frequency array imaging. This method is an extension to multiple signal classification which uses (i) the noise subspace to determine the location and support of targets, and (ii) the signal subspace to recover quantitative information about the targets. For point targets, we are able to recover the complex reflectivity and for an extended target under the Born approximation, we are able to recover a scalar quantity that is related to the product of the volume and relative dielectric permittivity of the target. Our resolution analysis for a point target demonstrates this method is capable of achieving exact recovery of the complex reflectivity at subwavelength resolution. Additionally, this resolution analysis shows that noise in the data effectively acts as a regularization to the imaging functional resulting in a method that is surprisingly more robust and effective with noise than without noise.

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18

Nguyen, Hoai-Thu, Sylvain Grange, Benjamin Leporq, Magalie Viallon, Pierre Croisille, and Thomas Grenier. "Impact of Distortion on Local Radiomic Analysis of Quadriceps Based on Quantitative Magnetic Resonance Imaging Data." International Journal of Pharma Medicine and Biological Sciences 10, no. 2 (April 2021): 49–54. http://dx.doi.org/10.18178/ijpmbs.10.2.49-54.

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19

Schubert, Martin C., Holger Habenicht, Michael J. Kerler, and Wilhelm Warta. "Quantitative Iron Concentration Imaging." Solid State Phenomena 156-158 (October 2009): 407–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.156-158.407.

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Iron concentration imaging has been proven to be a very valuable analysis technique for silicon material characterization. We applied this method to determine the influence of a low temperature annealing after surface passivation on the interstitial iron concentration. The influence of hydrogen passivation induced by silicon nitride passivation is estimated by comparison of silicon nitride and aluminum oxide passivation. The second part of this work deals with systematic errors inherent to the iron concentration technique. Simulations show under which conditions errors occur due to the non-uniformity of carrier profiles.

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20

Padikkalakandy Cheriyath, Shesnia Salim. "Texture Analysis of Quantitative adc Maps to Differentiate Low from High Grade Glioma." Clinical Research and Clinical Trials 6, no. 2 (June 27, 2022): 01–05. http://dx.doi.org/10.31579/2693-4779/096.

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Accurate discrimination between high grade gliomas (HGG) and metastatic brain tumor (MET) using noninvasive imaging is essential for selecting appropriate surgical and radiotherapy treatments and for determining the treatment response.

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21

Luo, Li, Xue Zheng, Kai-Zhong Tao, Jiang Zhang, Yue-Yang Tang, and Fu-Gang Han. "Imaging Analysis of Ganglioneuroma and Quantitative Analysis of Paraspinal Ganglioneuroma." Medical Science Monitor 25 (July 15, 2019): 5263–71. http://dx.doi.org/10.12659/msm.916792.

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22

Zaidi, Habib. "Quantitative Analysis in Multimodality Imaging: Challenges and Opportunities." Medical Technologies Journal 1, no. 1 (March 28, 2017): 4–5. http://dx.doi.org/10.26415/2572-004x-vol1iss1p4-5.

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23

Shindo, D., T. Ohishi, S. Iijima, K. Hiraga, T. Oikawa, and M. Kerskei. "Quantitative analysis of diffuse scattering with imaging plate." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 1992): 1188–89. http://dx.doi.org/10.1017/s0424820100130572.

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The excellent properties of the imaging plate (IP), i.e., a wide dynamic range and good linearity for the electron intensity, are promising for quantitative analysis of diffuse scattering since they allow a small dynamical diffraction effect to be evaluated with ‘dynamical factor’. In this paper, we first present accurate measurement of thermal diffuse scattering (TDS), which contributes dominantly to the background of electron diffraction patterns. Secondly, we present a method of extracting weak signal scattering intensities from the background, and apply it to the analysis of diffuse scattering caused from short-range ordered structures.Electron microscope images were obtained with a JEM-2000EXII electron microscope. Image processing was carried out by using a computer system (ACOS 2020) at Tohoku University. Electron diffraction patterns were recorded by using the TEM-IP system (PIXsysTEM). The details of the IP data handling were presented in the previous paper.Figure 1 shows an electron diffraction pattern obtained from an Au thin film ( t ∼ 40nm) at room temperature.

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24

Tarr, Jonathan, Koichi Nishikida, and Federico Izzia. "Infrared Chemical Imaging: Semi-Quantitative Analysis of Components." Microscopy Today 14, no. 4 (July 2006): 48–51. http://dx.doi.org/10.1017/s1551929500050288.

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With new image analysis software for infrared spectroscopy, semi-quantitative studies can be performed without the use of standards providing rapid measurements of component concentrations when an approximate value is suitable or when quantitative standards are not available. Infrared spectroscopy has been used for many years to identify the composition of organic and other materials. Single or multi-component samples can be analyzed and their chemical structures determined by comparing their absorbance peaks to known spectral libraries or fundamental molecular transitions. This technique is used throughout the scientific community and provides rich information.

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25

Quarantelli, Mario, and Anna Prinster. "Habib Zaidi: Quantitative Analysis in Nuclear Medicine Imaging." European Journal of Nuclear Medicine and Molecular Imaging 34, no. 10 (March 30, 2007): 1708. http://dx.doi.org/10.1007/s00259-007-0410-9.

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26

Li, Xiumei, Guoan Bi, and Yingtuo Ju. "Quantitative SNR Analysis for ISAR Imaging using LPFT." IEEE Transactions on Aerospace and Electronic Systems 45, no. 3 (July 2009): 1241–48. http://dx.doi.org/10.1109/taes.2009.5259197.

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27

Wouters, Fred S., and Alessandro Esposito. "Quantitative analysis of fluorescence lifetime imaging made easy." HFSP Journal 2, no. 1 (February 2008): 7–11. http://dx.doi.org/10.2976/1.2833600.

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28

Chen, Quan, Frank R. Rack, and Bruce J. Balcom. "Quantitative magnetic resonance imaging methods for core analysis." Geological Society, London, Special Publications 267, no. 1 (2006): 193–207. http://dx.doi.org/10.1144/gsl.sp.2006.267.01.14.

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29

Boudreau, Mathieu, Nikola Stikov, and G. Bruce Pike. "B1 -sensitivity analysis of quantitative magnetization transfer imaging." Magnetic Resonance in Medicine 79, no. 1 (March 27, 2017): 276–85. http://dx.doi.org/10.1002/mrm.26673.

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30

Zhang, Xiao. "Digital imaging and quantitative image analysis of polymer blends." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 170–71. http://dx.doi.org/10.1017/s0424820100163319.

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Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.

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31

Le, Thanh Dat, Seong-Young Kwon, and Changho Lee. "Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review." Photonics 9, no. 3 (March 11, 2022): 176. http://dx.doi.org/10.3390/photonics9030176.

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Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.

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32

Dam, Erik B. "Quantitative Automated Musculoskeletal Analysis." Academic Radiology 14, no. 10 (October 2007): 1153–55. http://dx.doi.org/10.1016/j.acra.2007.07.006.

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33

Hsiao, Wen-Tien, Hsin-Hon Lin, and Lu-Han Lai. "Application of Visual Radiographic Analysis of Quality Grade of Table Eggs." Applied Sciences 11, no. 6 (March 22, 2021): 2815. http://dx.doi.org/10.3390/app11062815.

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Digital radiography is currently the main method of medical imaging diagnosis. It also has a wide range of applications across different fields. This study used radiation to conduct non-destructive visual imaging, and further established a quantitative analysis for visual gray-scale images to determine changes in the quality of eggs. Eggs of the same weight with three quality classes were chosen according to the egg labels available on the market. Furthermore, a general medical X-ray digital imaging system was used to apply two-dimensional digital radiography. A photometric interpretation of monochrome gray-scale imaging established by the Digital Imaging and Communications in Medicine (DICOM) standard was used to conduct a quantitative stratification analysis of the matrix data visualization, along with one-way analysis of variance (ANOVA) for quantitative statistics of the gray-scale values for the three structures, i.e., shell, air cell, and yolk. The statistical results showed that X-ray digital gray-scale images and a quantitative stratification analysis of the matrix data visualization results are less easily identified based on visual differences. In the quantitative statistical results of the one-way ANOVA gray-scale values, the whole-egg and in-egg quantitative matrix analysis both show p < 0.05. In the analysis of egg freshness, the quantitative statistics of the percentage of space occupied by the air cell in the eggs also showed p < 0.05. In addition, the results of the freshness of each egg were graded. The quality and freshness of the eggs can be quantitatively analyzed through radiographic imaging. The results of this study will provide a more scientific and quantitative reference for the quality and freshness of agricultural products in the future.

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34

Shi, Zhang, Jing Li, Ming Zhao, Wenjia Peng, Zakaria Meddings, Tao Jiang, Qi Liu, Zhongzhao Teng, and Jianping Lu. "Quantitative Histogram Analysis on Intracranial Atherosclerotic Plaques." Stroke 51, no. 7 (July 2020): 2161–69. http://dx.doi.org/10.1161/strokeaha.120.029062.

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Background and Purpose: Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging. Methods: Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs. Results: In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of <70%. Multiple logistic regression analyses showed that intraplaque hemorrhage (OR, 16.294 [95% CI, 1.043–254.632]; P =0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032–2.087]; P =0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987–45.204]; P <0.001) were 3 significant features in defining culprit plaques in middle cerebral artery. The enhancement ratio (OR, 9.476 [95% CI, 1.256–71.464]; P =0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971–10.203]; P =0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820–21.343]; P <0.001) were significantly associated with plaque type in basilar artery. Coefficient of variation was a strong independent predictor in defining plaque type for both middle cerebral artery and basilar artery with sensitivity, specificity, and accuracy being 0.79, 0.80, and 0.80, respectively. Conclusions: Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.

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35

Schrag, Benaiah D., Xiaoyong Liu, Jan S. Hoftun, Peter L. Klinger, T. M. Levin, and David P. Vallett. "Quantitative Analysis and Depth Measurement via Magnetic Field Imaging." EDFA Technical Articles 7, no. 4 (November 1, 2005): 24–31. http://dx.doi.org/10.31399/asm.edfa.2005-4.p024.

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Abstract Magnetic field imaging is proving to be a valuable tool for semiconductor failure analysts and test engineers. One of its main advantages is that it does not require sample preparation or deprocessing because magnetic fields pass through most materials used in ICs and device packages. This article discusses the theory and practical limitations of magnetic field imaging and demonstrates its use in mapping current density and determining the location and depth of current-carrying conductors.

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36

Kim, Suree, Soohee Choi, and Dongmin Kang. "Quantitative and qualitative analysis of autophagy flux using imaging." BMB Reports 53, no. 5 (May 31, 2020): 241–47. http://dx.doi.org/10.5483/bmbrep.2020.53.5.046.

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37

Di Giuliano, Francesca, Silvia Minosse, Eliseo Picchi, Valentina Ferrazzoli, Valerio Da Ros, Massimo Muto, Chiara Adriana Pistolese, Francesco Garaci, and Roberto Floris. "Qualitative and quantitative analysis of 3D T1 Silent imaging." La radiologia medica 126, no. 9 (June 15, 2021): 1207–15. http://dx.doi.org/10.1007/s11547-021-01380-6.

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38

Nesterets, Ya I., P. Coan, T. E. Gureyev, A. Bravin, P. Cloetens, and S. W. Wilkins. "On qualitative and quantitative analysis in analyser-based imaging." Acta Crystallographica Section A Foundations of Crystallography 62, no. 4 (June 21, 2006): 296–308. http://dx.doi.org/10.1107/s0108767306017843.

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39

Ahmadi, J., S. Destian, M. L. Apuzzo, H. D. Segall, and C. S. Zee. "Cystic fluid in craniopharyngiomas: MR imaging and quantitative analysis." Radiology 182, no. 3 (March 1992): 783–85. http://dx.doi.org/10.1148/radiology.182.3.1535894.

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40

Velthuizen, R. P. "Quantitative analysis of brain tissue volumes with MR imaging." Radiology 183, no. 3 (June 1992): 876–77. http://dx.doi.org/10.1148/radiology.183.3.1584952.

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41

Karabiyik, Yucel, Ingvild Kinn Ekroll, Sturla H. Eik-Nes, and Lasse Lovstakken. "Quantitative Doppler Analysis Using Conventional Color Flow Imaging Acquisitions." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 65, no. 5 (May 2018): 697–708. http://dx.doi.org/10.1109/tuffc.2018.2808226.

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42

Baffou, Guillaume, Pierre Bon, Julien Savatier, Julien Polleux, Min Zhu, Marine Merlin, Hervé Rigneault, and Serge Monneret. "Thermal Imaging of Nanostructures by Quantitative Optical Phase Analysis." ACS Nano 6, no. 3 (February 15, 2012): 2452–58. http://dx.doi.org/10.1021/nn2047586.

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43

Zamora, L. Lahuerta, P. Alemán López, G. M. Antón Fos, R. Martín Algarra, A. M. Mellado Romero, and J. Martínez Calatayud. "Quantitative colorimetric-imaging analysis of nickel in iron meteorites." Talanta 83, no. 5 (February 2011): 1575–79. http://dx.doi.org/10.1016/j.talanta.2010.11.058.

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44

Yang, Zhi, Theresa A. Tuthill, David L. Raunig, Martin D. Fox, and Mostafa Analoui. "Pixel Compounding: Resolution-Enhanced Ultrasound Imaging for Quantitative Analysis." Ultrasound in Medicine & Biology 33, no. 8 (August 2007): 1309–19. http://dx.doi.org/10.1016/j.ultrasmedbio.2007.02.013.

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45

Roux, E., M. Deloume, R. Markovic, M. Gosak, M. Marhl, C. Duplàa, and T. Couffinhal. "Quantitative analysis of 3D imaging of mouse coronary vasculature." Archives of Cardiovascular Diseases Supplements 9, no. 2 (April 2017): 215. http://dx.doi.org/10.1016/s1878-6480(17)30533-5.

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46

Ebel, Maria F., Horst Ebel, Guido Barnegg-Golwig, Michael Mantler, and Robert Svagera. "Quantitative surface analysis performed with an imaging photoelectron spectrometer." Mikrochimica Acta 101, no. 1-6 (January 1990): 63–69. http://dx.doi.org/10.1007/bf01244159.

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47

Akbari, Hamed, Luma V. Halig, David M. Schuster, Adeboye Osunkoya, Viraj Master, Peter T. Nieh, Georgia Z. Chen, and Baowei Fei. "Hyperspectral imaging and quantitative analysis for prostate cancer detection." Journal of Biomedical Optics 17, no. 7 (July 6, 2012): 0760051. http://dx.doi.org/10.1117/1.jbo.17.7.076005.

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48

Xing, D., S. J. Gibbs, J. A. Derbyshire, E. J. Fordham, T. A. Carpenter, and L. D. Hall. "Bayesian Analysis for Quantitative NMR Flow and Diffusion Imaging." Journal of Magnetic Resonance, Series B 106, no. 1 (January 1995): 1–9. http://dx.doi.org/10.1006/jmrb.1995.1001.

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49

Hedegaard, Martin A. B., Mads S. Bergholt, and Molly M. Stevens. "Quantitative multi-image analysis for biomedical Raman spectroscopic imaging." Journal of Biophotonics 9, no. 5 (February 2, 2016): 542–50. http://dx.doi.org/10.1002/jbio.201500238.

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50

Subhani, S. K., B. Suresh, and V. S. Ghali. "Quantitative subsurface analysis using frequency modulated thermal wave imaging." Infrared Physics & Technology 88 (January 2018): 41–47. http://dx.doi.org/10.1016/j.infrared.2017.10.009.

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