Relevant bibliographies by topics / Computational methods in biomedical optical imaging

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Computational methods in biomedical optical imaging'

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Published: 6 September 2023

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Journal articles on the topic "Computational methods in biomedical optical imaging"

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Liu, Xueyan, Dong Peng, Wei Guo, Xibo Ma, Xin Yang, and Jie Tian. "Compressed Sensing Photoacoustic Imaging Based on Fast Alternating Direction Algorithm." International Journal of Biomedical Imaging 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/206214.

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Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.
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Laurino, Annunziatina, Alessandra Franceschini, Luca Pesce, Lorenzo Cinci, Alberto Montalbano, Giacomo Mazzamuto, Giuseppe Sancataldo, et al. "A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy." International Journal of Molecular Sciences 24, no. 7 (April 4, 2023): 6747. http://dx.doi.org/10.3390/ijms24076747.

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The analysis of histological alterations in all types of tissue is of primary importance in pathology for highly accurate and robust diagnosis. Recent advances in tissue clearing and fluorescence microscopy made the study of the anatomy of biological tissue possible in three dimensions. The combination of these techniques with classical hematoxylin and eosin (H&E) staining has led to the birth of three-dimensional (3D) histology. Here, we present an overview of the state-of-the-art methods, highlighting the optimal combinations of different clearing methods and advanced fluorescence microscopy techniques for the investigation of all types of biological tissues. We employed fluorescence nuclear and eosin Y staining that enabled us to obtain hematoxylin and eosin pseudo-coloring comparable with the gold standard H&E analysis. The computational reconstructions obtained with 3D optical imaging can be analyzed by a pathologist without any specific training in volumetric microscopy, paving the way for new biomedical applications in clinical pathology.
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Zaitsev, Vladimir Y., Sergey Y. Ksenofontov, Alexander A. Sovetsky, Alexander L. Matveyev, Lev A. Matveev, Alexey A. Zykov, and Grigory V. Gelikonov. "Real-Time Strain and Elasticity Imaging in Phase-Sensitive Optical Coherence Elastography Using a Computationally Efficient Realization of the Vector Method." Photonics 8, no. 12 (November 24, 2021): 527. http://dx.doi.org/10.3390/photonics8120527.

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We present a real-time realization of OCT-based elastographic mapping local strains and distribution of the Young’s modulus in biological tissues, which is in high demand for biomedical usage. The described variant exploits the principle of Compression Optical Coherence Elastography (C-OCE) and uses processing of phase-sensitive OCT signals. The strain is estimated by finding local axial gradients of interframe phase variations. Instead of the popular least-squares method for finding these gradients, we use the vector approach, one of its advantages being increased computational efficiency. Here, we present a modified, especially fast variant of this approach. In contrast to conventional correlation-based methods and previously used phase-resolved methods, the described method does not use any search operations or local calculations over a sliding window. Rather, it obtains local strain maps (and then elasticity maps) using several transformations represented as matrix operations applied to entire complex-valued OCT scans. We first elucidate the difference of the proposed method from the previously used correlational and phase-resolved methods and then describe the proposed method realization in a medical OCT device, in which for real-time processing, a “typical” central processor (e.g., Intel Core i7-8850H) is sufficient. Representative examples of on-flight obtained elastographic images are given. These results open prospects for broad use of affordable OCT devices for high-resolution elastographic vitalization in numerous biomedical applications, including the use in clinic.
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Sridhar, Chethana, Piyush Kumar Pareek, R. Kalidoss, Sajjad Shaukat Jamal, Prashant Kumar Shukla, and Stephen Jeswinde Nuagah. "Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ." Journal of Healthcare Engineering 2022 (February 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/2354866.

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Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.
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Hauptman, Ami, Ganesh M. Balasubramaniam, and Shlomi Arnon. "Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming." Bioengineering 10, no. 3 (March 21, 2023): 382. http://dx.doi.org/10.3390/bioengineering10030382.

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Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called “XGBoost” to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.
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Jiang, Yuan, Hao Sha, Shuai Liu, Peiwu Qin, and Yongbing Zhang. "AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging." Biomedical Optics Express 14, no. 9 (August 22, 2023): 4814. http://dx.doi.org/10.1364/boe.498421.

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Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models, and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A transfer learning framework is further devised to allow our AutoUnmix to adapt to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve the highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. For experiments where spectral profiles and morphology are akin to simulated data, our method realizes the quantitative performance demonstrated above. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool for studying the interaction process of different organelles labeled by multiple fluorophores.
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Akman, Ozgur E., Steven Watterson, Andrew Parton, Nigel Binns, Andrew J. Millar, and Peter Ghazal. "Digital clocks: simple Boolean models can quantitatively describe circadian systems." Journal of The Royal Society Interface 9, no. 74 (April 12, 2012): 2365–82. http://dx.doi.org/10.1098/rsif.2012.0080.

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The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day–night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.
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Mostaço-Guidolin, Leila B., Michael S. D. Smith, Mark Hewko, Bernie Schattka, Michael G. Sowa, Arkady Major, and Alex C. T. Ko. "Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging." Journal of Applied Physiology 126, no. 3 (March 1, 2019): 638–46. http://dx.doi.org/10.1152/japplphysiol.00497.2018.

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Structural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer. NEW & NOTEWORTHY Textural analyses such as fractal dimension (FD) and directional analysis (DA) are straightforward and computationally viable strategies to extract fiber-related morphological data from optical images. Therefore, objective, quantitative, and automated characterization of protein fiber morphology in extracellular matrix can be realized by using these methods in combination with digital imaging techniques such as nonlinear optical microscopy (NLOM), a highly effective visualization tool for fibrillar collagen and elastic network. Combining FD and DA with NLOM is an innovative approach to track alterations of structural fibrillar proteins. The results illustrated in this study not only prove the effectiveness of FD and DA methods in extracellular protein characterization but also demonstrate their potential value in clinical and basic biomedical research where protein microstructure characterization is critical.
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Zhang, Huiting, Dong-Hee Kang, Marie Piantino, Daisuke Tominaga, Takashi Fujimura, Noriyuki Nakatani, J. Nicholas Taylor, Tomomi Furihata, Michiya Matsusaki, and Satoshi Fujita. "Rapid Quantification of Microvessels of Three-Dimensional Blood–Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm." Biosensors 13, no. 8 (August 15, 2023): 818. http://dx.doi.org/10.3390/bios13080818.

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The blood–brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.
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Chen, Duan, Guo-Wei Wei, Wen-Xiang Cong, and Ge Wang. "Computational methods for optical molecular imaging." Communications in Numerical Methods in Engineering 25, no. 12 (December 2009): 1137–61. http://dx.doi.org/10.1002/cnm.1164.

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Dissertations / Theses on the topic "Computational methods in biomedical optical imaging"

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Birch, Gabriel C. "Computational and Design Methods for Advanced Imaging." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/242355.

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This dissertation merges the optical design and computational aspects of imaging systems to create novel devices that solve engineering problems in optical science and attempts to expand the solution space available to the optical designer. This dissertation is divided into two parts: the first discusses a new active illumination depth sensing modality, while the second part discusses a passive illumination system called plenoptic, or lightfield, imaging. The new depth sensing modality introduced in part one is called depth through controlled aberration. This technique illuminates a target with a known, aberrated projected pattern and takes an image using a traditional, unmodified imaging system. Knowing how the added aberration in the projected pattern changes as a function of depth, we are able to quantitatively determine depth of a series of points from the camera. A major advantage this method permits is the ability for illumination and imaging axes to be coincident. Plenoptic cameras capture both spatial and angular data simultaneously. This dissertation present a new set of parameters that permit the design and comparison of plenoptic devices outside the traditionally published plenoptic 1.0 and plenoptic 2.0 configurations. Additionally, a series of engineering advancements are presented, including full system ray traces of raw plenoptic images, Zernike compression techniques of raw image files, and non-uniform lenslet arrays to compensate for plenoptic system aberrations. Finally, a new snapshot imaging spectrometer is proposed based off the plenoptic configuration.
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Balagopal, Bavishna. "Advanced methods for enhanced sensing in biomedical Raman spectroscopy." Thesis, University of St Andrews, 2014. http://hdl.handle.net/10023/6343.

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Raman spectroscopy is a powerful tool in the field of biomedicine for disease diagnosis owing to its potential to provide the molecular fingerprint of biological samples. However due to the inherent weak nature of the Raman process, there is a constant quest for enhancing the sensitivity of this technique for enhanced diagnostic efficiency. This thesis focuses on achieving this goal by integrating advanced methods with Raman spectroscopy. Firstly this thesis explores the applicability of a laser based fluorescence suppression technique – Wavelength Modulated Raman Spectroscopy (WMRS) - for suppressing the broad luminescence background which often obscure the Raman peaks. The WMRS technique was optimized for its applications in single cell studies and tissue studies for enhanced sensing without compromising the throughput. It has been demonstrated that the optimized parameter would help to chemically profile single cell within 6 s. A two fold enhancement in SNR of Raman bands was demonstrated when WMRS was implemented in fiber Raman based systems for tissue analysis. The suitability of WMRS on highly sensitive single molecule detection techniques such as Surface Enhanced Raman Spectroscopy (SERS) and Surface Enhanced Resonance Raman Spectroscopy (SERRS) was also explored. Further this optimized technique was successfully used to address an important biological problem in the field of immunology. This involved label-free identification of major immune cell subsets from human blood. Later part of this thesis explores a multimodal approach where Raman spectroscopy was combined with Optical Coherence Tomography (OCT) for enhanced diagnostic sensitivity (>10%). This approach was used to successfully discriminate between ex-vivo adenocarcinoma tissues and normal colon tissues. Finally this thesis explores the design and implementation of a specialized fiber Raman probe that is compatible with surgical environments. This probe was originally developed to be compatible with Magnetic Resonance Imaging (MRI) environment. It has the potential to be used for performing minimally invasive optical biopsy during interventional MRI procedures.
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Jones, Cameron Christopher. "VALIDATION OF COMPUTATIONAL FLUID DYNAMIC SIMULATIONS OF MEMBRANE ARTIFICIAL LUNGS WITH X-RAY IMAGING." UKnowledge, 2012. http://uknowledge.uky.edu/cbme_etds/2.

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The functional performance of membrane oxygenators is directly related to the perfusion dynamics of blood flow through the fiber bundle. Non-uniform flow and design characteristics can limit gas exchange efficiency and influence susceptibility of thrombus development in the fiber membrane. Computational fluid dynamics (CFD) is a powerful tool for predicting properties of the flow field based on prescribed geometrical domains and boundary conditions. Validation of numerical results in membrane oxygenators has been predominantly based on experimental pressure measurements with little emphasis placed on confirmation of the velocity fields due to opacity of the fiber membrane and limitations of optical velocimetric methods. A novel approach was developed using biplane X-ray digital subtraction angiography to visualize flow through a commercial membrane artificial lung at 1–4.5 L/min. Permeability based on the coefficients of the Ergun equation, α and β, were experimentally determined to be 180 and 2.4, respectively, and the equivalent spherical diameter was shown to be approximately equal to the outer fiber diameter. For all flow rates tested, biplane image projections revealed non-uniform radial perfusion through the annular fiber bundle, yet without flow bias due to the axisymmetric position of the outlet. At 1 L/min, approximately 78.2% of the outward velocity component was in the radial (horizontal) plane verses 92.0% at 4.5 L/min. The CFD studies were unable to predict the non-radial component of the outward perfusion. Two-dimensional velocity fields were generated from the radiographs using a cross-correlation tracking algorithm and compared with analogous image planes from the CFD simulations. Velocities in the non-porous regions differed by an average of 11% versus the experimental values, but simulated velocities in the fiber bundle were on average 44% lower than experimental. A corrective factor reduced the average error differences in the porous medium to 6%. Finally, biplane image pairs were reconstructed to show 3-D transient perfusion through the device. The methods developed from this research provide tools for more accurate assessments of fluid flow through membrane oxygenators. By identifying non-invasive techniques to allow direct analysis of numerical and experimental velocity fields, researchers can better evaluate device performance of new prototype designs.
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Montejo, Ludguier. "Computational Methods For The Diagnosis of Rheumatoid Arthritis With Diffuse Optical Tomography." Thesis, 2014. https://doi.org/10.7916/D8NS0S0C.

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Diffuse optical tomography (DOT) is an imaging technique where near infrared (NIR) photons are used to probe biological tissue. DOT allows for the recovery of three-dimensional maps of tissue optical properties, such as tissue absorption and scattering coefficients. The application of DOT as a tool to aid in the diagnosis of rheumatoid arthritis (RA) is explored in this work. Algorithms for improving the image reconstruction process and for enhancing the clinical value of DOT images are presented in detail. The clinical data considered in this work consists of 99 fingers from subjects with RA and 120 fingers from healthy subjects. DOT scans of the proximal interphalangeal (PIP) joint of each finger is performed with modulation frequencies of 0, 300, and 600 MHz. A computer-aided diagnosis (CAD) framework for extracting heuristic features from DOT images and a method for using these same features to classify each joint as affected or not affected by RA is presented. The framework is applied to the clinical data and results are discussed in detail. Then, an algorithm for recovering the optical properties of biological media using the simplified spherical harmonics (SPN) light propagation model is presented. The computational performance of the algorithm is analyzed and reported. Finally, the SPN reconstruction algorithm is applied to clinical data of subjects with RA and the resulting images are analyzed with the CAD framework. As the first part of the CAD framework, heuristic image features are extracted from the absorption and the scattering coefficient images using multiple compression and dimensionality reduction techniques. Overall, 594 features are extracted from the images of each joint. Then, machine-learning techniques are used to evaluate the ability to discriminate between images of joints with RA and images of healthy joints. An evolution-strategy optimization algorithm is developed to evaluate the classification strength of each feature and to find the multidimensional feature combination that results in optimal classification accuracy. Classification is performed with k-nearest neighbors (KNN), linear (LDA) and quadratic discriminate analysis (QDA), self-organizing maps (SOM), or support vector machines (SVM). Classification accuracy is evaluated based on diagnostic sensitivity and specificity values. Strong evidence is presented that suggest there are clear differences between the tissue optical parameters of joints with RA and joints without RA. It is first shown that data obtained at 600 MHz leads to better classification results than data obtained at 300 and 0 MHz. Analysis of each extracted feature shows that DOT images of subjects with RA are statistically different (p < 0.05) from images of subjects without RA for over 90% of the features. Evidence shows that subjects with RA that do not have detectable signs of erosion, effusion, or synovitis (i.e. asymptomatic subjects) in MRI and US images have optical profiles similar to subjects who do have signs of erosion, effusion, or synovitis; furthermore, both of these cohorts differ from healthy controls subjects. This shows that it may be possible to accurately identify asymptomatic subjects with DOT scans. In contrast, these subjects remain difficult to identify from MRI and US images. The implications of these results are profound, as they suggest it may be possible to identify RA with DOT at an earlier stage compared to standard imaging techniques. Results from the feature-selection algorithm show that the SVM algorithm (with a third order polynomial kernel) achieves 100.0% sensitivity and 97.8% specificity. Lower bounds for these results (at 95.0% confidence level) are 96.4% and 93.8%, respectively. Image features most predictive of RA are from the spatial variation of optical properties and the absolute range in feature values. The optimal classifiers are low dimensional combinations (< 7 features). Robust cross- validation is performed to ensure the generalization of these classification results. The SPN -based reconstruction algorithm uses a reduced-Hessian sequential quadratic programming (rSQP) PDE-constrained optimization approach to maximize computational efficiency. The complex-valued forward model, or frequency domain SPN equations (N = 1, 3), is discretized using the finite-volume method and solved on unstructured computational grids using the restarted GMRES algorithm. The image reconstruction algorithm is presented in detail and its performance benchmarked against the ERT algorithm. The algorithm is subsequently used to recover the absorption and scattering coefficient images of joints scanned in the RA clinical study. While the SPN model is inherently less accurate than the ERT model, it is nevertheless shown that the images obtained with the SP3-based reconstruction algorithm are sufficiently accurate and allow for the diagnosis of RA at clinically relevant sensitivity [87.9% (78.1%, 100.0%)] and specificity [92.9% (84.6%, 100.0%)] values (the 95.0% confidence interval is specified in brackets). In contrast to results obtained with the SP3 model, the images generated with the SP1 algorithm yield significantly lower sensitivity [66.7% (46.6%, 100.0%)] and specificity [81.0% (64.8%, 100.0%)] values. While some numerical accuracy is sacrificed by selecting the SP3 model over the ERT model, the superior computational performance of the SP3 algorithm allows for computation of the absorption and the scattering coefficient images in under 15 minutes and requires less than 200 MB of RAM per finger (compared to the over 180 minutes and over 6 GB of RAM needed by the ERT-based algorithm). Overall, results indicate that the SP3-based reconstruction algorithm provides computational advantages over the ERT-based algorithm without sacrificing significant classification accuracy. In contrast, the SP1 model provides computational advantages compared to the ERT at the expense of classification accuracy. This indicates that the frequency-domain SP3 model is an ideal light propagation model for use in DOT scanning of finger joints with RA. Altogether, the results presented in this dissertation underscore the high potential for DOT to become a clinically useful diagnostic tool. The algorithms and framework developed as part of this dissertation can be directly used on future data to help further validate the hypotheses presented in this work and to further establish DOT imaging as a valuable diagnostic tool.
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Ravi, Prasad K. J. "Development of Efficient Computational Methods for Better Estimation of Optical Properties in Diffuse Optical Tomography." Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3311.

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Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro- vides functional information of the soft biological tissues in-vivo, such as breast and brain tissues. The near infrared (NIR) light (600-1000 nm) is the interrogating radiation, which is typically delivered and collected using fiber bundles placed on the boundary of the tissue. The internal optical property distribution is estimated via model-based image reconstruction algorithm using these limited boundary measurements. Image reconstruction problem in DOT is known to be non-linear, ill-posed, and some times under-determined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of the regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of non-quadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases were obtained automatically using the generalized cross-validation (GCV) method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that while quadratic penalty may be able to provide better separation between two closely spaced targets, it's contrast recovery capability is limited and the sparseness promoting penalties, such as `1, Cauchy, Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets with Geman-McClure penalty being the most optimal one. Effective usage of image guidance by incorporating the refractive index (RI) variation in computational modeling of light propagation in tissue is investigated to assess its impact on optical-property estimation. With the aid of realistic patient breast three-dimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in image-guided diffuse optical tomography (IG-DOT) using numerical simulations. It is also shown that by assuming identical RI for all regions of tissue would lead to erroneous estimation of optical properties. The a priori knowledge of the RI for the segmented regions of tissue in IG-DOT, which is difficult to obtain for the in vivo cases, leads to more accurate estimates of optical properties. Even inclusion of approximated RI values, obtained from the literature, for the regions of tissue resulted in better estimates of optical properties, with values comparable to that of having the correct knowledge of RI for different regions of tissue. Image reconstruction in IG-DOT procedure involves reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem to be well-determined compared to traditional under- determined case. Still, the methods that are deployed in this case are same as the one used for traditional diffuse optical image reconstruction, which involves regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method was proposed here to perform the image reconstruction procedure and shown to be providing solutions that are closely matching with ones obtained using established methods. The proposed method also has the distinctive advantage of being more efficient due to being regularization free, involving only repeated forward calculations.
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Ravi, Prasad K. J. "Development of Efficient Computational Methods for Better Estimation of Optical Properties in Diffuse Optical Tomography." Thesis, 2013. http://etd.iisc.ernet.in/2005/3311.

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Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro- vides functional information of the soft biological tissues in-vivo, such as breast and brain tissues. The near infrared (NIR) light (600-1000 nm) is the interrogating radiation, which is typically delivered and collected using fiber bundles placed on the boundary of the tissue. The internal optical property distribution is estimated via model-based image reconstruction algorithm using these limited boundary measurements. Image reconstruction problem in DOT is known to be non-linear, ill-posed, and some times under-determined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of the regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of non-quadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases were obtained automatically using the generalized cross-validation (GCV) method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that while quadratic penalty may be able to provide better separation between two closely spaced targets, it's contrast recovery capability is limited and the sparseness promoting penalties, such as `1, Cauchy, Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets with Geman-McClure penalty being the most optimal one. Effective usage of image guidance by incorporating the refractive index (RI) variation in computational modeling of light propagation in tissue is investigated to assess its impact on optical-property estimation. With the aid of realistic patient breast three-dimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in image-guided diffuse optical tomography (IG-DOT) using numerical simulations. It is also shown that by assuming identical RI for all regions of tissue would lead to erroneous estimation of optical properties. The a priori knowledge of the RI for the segmented regions of tissue in IG-DOT, which is difficult to obtain for the in vivo cases, leads to more accurate estimates of optical properties. Even inclusion of approximated RI values, obtained from the literature, for the regions of tissue resulted in better estimates of optical properties, with values comparable to that of having the correct knowledge of RI for different regions of tissue. Image reconstruction in IG-DOT procedure involves reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem to be well-determined compared to traditional under- determined case. Still, the methods that are deployed in this case are same as the one used for traditional diffuse optical image reconstruction, which involves regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method was proposed here to perform the image reconstruction procedure and shown to be providing solutions that are closely matching with ones obtained using established methods. The proposed method also has the distinctive advantage of being more efficient due to being regularization free, involving only repeated forward calculations.
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Gutta, Sreedevi. "Improving photoacoustic imaging with model compensating and deep learning methods." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/4390.

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Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and molecular information of the tissue under investiga- tion. The important step in photoacoustic tomography is image reconstruction, which enables quanti cation of tissue functional properties. The photoacoustic image recon- struction problem is typically ill-posed and requires an utilization of regularization to provide meaningful results. The aim of this thesis work is to develop methods that can improve photoacoustic image reconstruction, especially in realistic imaging scenar- ios, where the utility of standard image reconstruction methods is limited in terms of providing good quality photoacoustic images. The photoacoustic image reconstruction problem is typically solved using either weighted or ordinary least squares (LS), with regularization term being added for stabil- ity, which account only for data imperfections (noise). Numerical modeling of acoustic wave propagation requires discretization of imaging region and is typically developed based on many assumptions, such as speed of sound being constant in the tissue, making it imperfect. Two variants of total least squares (TLS) were proposed, namely ordinary TLS and Sparse TLS, which account for model imperfections. The ordinary TLS is implemented in the Lanczos bidiagonalization framework to make it computationally efficient. The Sparse TLS utilizes the total variation penalty to promote recovery of high frequency components in the re- constructed image. The Lanczos truncated TLS (Lanczos T-TLS) and Sparse TLS methods were compared with the recently established state-of-the-art methods, such as Lanczos Tikhonov and Exponential Filtering. The TLS methods exhibited better performance for experimental data as well as in cases where modeling errors were present, such as few acoustic detectors malfunctioning and speed of sound variations. Also, the TLS methods do not require any prior information about the errors present in the model or data, making it attractive for real-time scenarios. The model-based reconstruction methods, such as Tikhonov regularization scheme, require an appropriate selection of explicit regularization parameter, which is a com- putationally expensive procedure. The Tikhonov scheme promotes the smooth features in the reconstructed image due to the smooth regularizer, thus leading to loss of sharp features. A simple and computationally efficient extrapolation method was developed, which provides the solution at zero regularization, by assuming that the solution is a function of regularization. The reconstructed results using this method were shown in three variants (Lanczos, Traditional, and Exponential) of Tikhonov ltering on numer- ical and experimental phantom data. The proposed extrapolation method performance was shown to be superior than the standard error estimate technique with an added advantage of being atleast four times faster in terms of computation, and providing an improvement as high as 2.6 times in terms of standard gures of merit. Photoacoustic signals collected at the boundary of tissue are always band-limited. A deep neural network (DNN) with ve fully connected layers (similar to the decoder network) was proposed to enhance the bandwidth of the detected photoacoustic signal, thereby improving the quantitative accuracy of the reconstructed photoacoustic images. A least square based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The DNN-based method was evaluated using both numerical and experimental data. The results show that the DNN-based method was capable of enhancing the bandwidth of the detected photoa- coustic signal, which in turn improves the contrast recovery and quality of reconstructed photoacoustic images without adding any signi cant computational burden. Analytical photoacoustic image reconstruction methods such as back-projection re- quire large amount of data for accurate reconstruction of initial pressure distribution. Model-based iterative algorithms are proven to provide quantitatively accurate recon- structions compared to analytical methods in limited data cases. These methods start from an initial guess of the solution (obtained through analytical methods) and itera- tively improve the solution via applying regularization. These are challenging to deploy in real-time due to their high computational complexity and also difficulty in choosing optimal reconstruction parameters. A deep convolutional neural network, with archi- tecture similar to SRGAN, a generative adversarial network (GAN) to obtain images of super resolution (SR), was utilized in the photoacoustic image reconstruction pro- cess to provide desired image characteristics obtainable by model-based algorithms with computation effciency equal to analytical methods. The network was trained with back- projected reconstruction as input and output being ground truth image. The proposed method was evaluated using both numerical and experimental phantoms and was shown to be superior compared to the state-of-the-art model-based methods. Moreover, the proposed method takes approximately one second on the GPU, making the approach attractive in real-time.
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Narayana, Swamy Yamuna. "Studies on Kernel Based Edge Detection an Hyper Parameter Selection in Image Restoration and Diffuse Optical Image Reconstruction." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/3615.

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Computational imaging has been playing an important role in understanding and analysing the captured images. Both image segmentation and restoration has been in-tegral parts of computational imaging. The studies performed in this thesis has been focussed toward developing novel algorithms for image segmentation and restoration. Study related to usage of Morozov Discrepancy Principle in Di use Optical Imaging was also presented here to show that hyper parameter selection could be performed with ease. The Laplacian of Gaussian (LoG) and Canny operators use Gaussian smoothing be-fore applying the derivative operator for edge detection in real images. The LoG kernel was based on second derivative and is highly sensitive to noise when compared to the Canny edge detector. A new edge detection kernel, called as Helmholtz of Gaussian (HoG), which provides higher di suavity is developed in this thesis and it was shown that it is more robust to noise. The formulation of the developed HoG kernel is similar to LoG. It was also shown both theoretically and experimentally that LoG is a special case of HoG. This kernel when used as an edge detector exhibited superior performance compared to LoG, Canny and wavelet based edge detector for the standard test cases both in one- and two-dimensions. The linear inverse problem encountered in restoration of blurred noisy images is typically solved via Tikhonov minimization. The outcome (restored image) of such min-imitation is highly dependent on the choice of regularization parameter. In the absence of prior information about the noise levels in the blurred image, ending this regular-inaction/hyper parameter in an automated way becomes extremely challenging. The available methods like Generalized Cross Validation (GCV) may not yield optimal re-salts in all cases. A novel method that relies on minimal residual method for ending the regularization parameter automatically was proposed here and was systematically compared with the GCV method. It was shown that the proposed method performance was superior to the GCV method in providing high quality restored images in cases where the noise levels are high Di use optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. An automated method for selection of regularization/hyper parameter that incorporates Morozov discrepancy principle(MDP) into the Tikhonov method was proposed and shown to be a promising method for the dynamic Di use Optical Tomography.
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Narayana, Swamy Yamuna. "Studies on Kernel Based Edge Detection an Hyper Parameter Selection in Image Restoration and Diffuse Optical Image Reconstruction." Thesis, 2017. http://etd.iisc.ernet.in/2005/3615.

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Computational imaging has been playing an important role in understanding and analysing the captured images. Both image segmentation and restoration has been in-tegral parts of computational imaging. The studies performed in this thesis has been focussed toward developing novel algorithms for image segmentation and restoration. Study related to usage of Morozov Discrepancy Principle in Di use Optical Imaging was also presented here to show that hyper parameter selection could be performed with ease. The Laplacian of Gaussian (LoG) and Canny operators use Gaussian smoothing be-fore applying the derivative operator for edge detection in real images. The LoG kernel was based on second derivative and is highly sensitive to noise when compared to the Canny edge detector. A new edge detection kernel, called as Helmholtz of Gaussian (HoG), which provides higher di suavity is developed in this thesis and it was shown that it is more robust to noise. The formulation of the developed HoG kernel is similar to LoG. It was also shown both theoretically and experimentally that LoG is a special case of HoG. This kernel when used as an edge detector exhibited superior performance compared to LoG, Canny and wavelet based edge detector for the standard test cases both in one- and two-dimensions. The linear inverse problem encountered in restoration of blurred noisy images is typically solved via Tikhonov minimization. The outcome (restored image) of such min-imitation is highly dependent on the choice of regularization parameter. In the absence of prior information about the noise levels in the blurred image, ending this regular-inaction/hyper parameter in an automated way becomes extremely challenging. The available methods like Generalized Cross Validation (GCV) may not yield optimal re-salts in all cases. A novel method that relies on minimal residual method for ending the regularization parameter automatically was proposed here and was systematically compared with the GCV method. It was shown that the proposed method performance was superior to the GCV method in providing high quality restored images in cases where the noise levels are high Di use optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. An automated method for selection of regularization/hyper parameter that incorporates Morozov discrepancy principle(MDP) into the Tikhonov method was proposed and shown to be a promising method for the dynamic Di use Optical Tomography.
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Harmany, Zachary Taylor. "Computational Optical Imaging Systems: Sensing Strategies, Optimization Methods, and Performance Bounds." Diss., 2012. http://hdl.handle.net/10161/6135.

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The emerging theory of compressed sensing has been nothing short of a revolution in signal processing, challenging some of the longest-held ideas in signal processing and leading to the development of exciting new ways to capture and reconstruct signals and images. Although the theoretical promises of compressed sensing are manifold, its implementation in many practical applications has lagged behind the associated theoretical development. Our goal is to elevate compressed sensing from an interesting theoretical discussion to a feasible alternative to conventional imaging, a significant challenge and an exciting topic for research in signal processing. When applied to imaging, compressed sensing can be thought of as a particular case of computational imaging, which unites the design of both the sensing and reconstruction of images under one design paradigm. Computational imaging tightly fuses modeling of scene content, imaging hardware design, and the subsequent reconstruction algorithms used to recover the images.

This thesis makes important contributions to each of these three areas through two primary research directions. The first direction primarily attacks the challenges associated with designing practical imaging systems that implement incoherent measurements. Our proposed snapshot imaging architecture using compressive coded aperture imaging devices can be practically implemented, and comes equipped with theoretical recovery guarantees. It is also straightforward to extend these ideas to a video setting where careful modeling of the scene can allow for joint spatio-temporal compressive sensing. The second direction develops a host of new computational tools for photon-limited inverse problems. These situations arise with increasing frequency in modern imaging applications as we seek to drive down image acquisition times, limit excitation powers, or deliver less radiation to a patient. By an accurate statistical characterization of the measurement process in optical systems, including the inherent Poisson noise associated with photon detection, our class of algorithms is able to deliver high-fidelity images with a fraction of the required scan time, as well as enable novel methods for tissue quantification from intraoperative microendoscopy data. In short, the contributions of this dissertation are diverse, further the state-of-the-art in computational imaging, elevate compressed sensing from an interesting theory to a practical imaging methodology, and allow for effective image recovery in light-starved applications.


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Books on the topic "Computational methods in biomedical optical imaging"

1

V, Tuchin V., ed. Handbook of optical biomedical diagnostics. Bellingham: SPIE Press, 2002.

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Handbook of optical biomedical diagnostics. Bellingham, Washington: SPIE Press, 2016.

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Tavares, João Manuel R. S., and Paulo Rui Fernandes, eds. New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23073-9.

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V, Tuchin V., Izatt Joseph A, Fujimoto James G, and Society of Photo-optical Instrumentation Engineers., eds. Coherence domain optical methods in biomedical science and clinical applications V: 23-24 January 2001, San Jose, USA. Bellingham, Wash., USA: SPIE, 2001.

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Li, Xingde, Qingming Luo, and Gu Ying. Optics in health care and biomedical optics IV: 18-20 October 2010, Beijing, China. Edited by SPIE (Society), Zhongguo guang xue xue hui, Beijing gong ye xue yuan, Zhongguo ke xue ji shu xie hui, Guo jia zi ran ke xue ji jin wei yuan hui (China), and China. Guo jia ke xue ji shu bu. Bellingham, Wash: SPIE, 2010.

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V, Tuchin V., Izatt Joseph A, Fujimoto James G, Society of Photo-optical Instrumentation Engineers., and International Biomedical Optics Society, eds. Coherence domain optical methods in biomedical science and clinical applications IV: 24-26 January 2000, San Jose, California. Bellingham, Wash., USA: SPIE, 2000.

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V, Tuchin V., Izatt Joseph A, Fujimoto James G, and Society of Photo-optical Instrumentation Engineers., eds. Coherence domain optical methods in biomedical science and clinical applications VI: 21-23 January 2002, San Jose, USA. Bellingham, Wash: SPIE, 2002.

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V, Tuchin V., Izatt Joseph A, Society of Photo-optical Instrumentation Engineers., and International Biomedical Optics Society, eds. Proceedings of coherence domain optical methods in biomedical science and clinical applications II: 27-28 January 1998, San Jose, California. Bellingham, Wash., USA: SPIE, 1998.

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Antoni, Nowakowski, Kosmowski Bogdan B, Society of Photo-optical Instrumentation Engineers., Politechnika Gdańska. Katedra Inżynierii Biomedycznej., and Poland. Ministerstwo Nauki i Informatyzacji., eds. Optical methods, sensors, image processing, and visualization in medicine: 10-13 September, 2003, Gdansk, Poland. Bellingham, Wash: SPIE, 2004.

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Handbook of biomedical optics. Boca Raton: CRC Press, 2011.

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Book chapters on the topic "Computational methods in biomedical optical imaging"

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Garini, Yuval, and Elad Tauber. "Spectral Imaging: Methods, Design, and Applications." In Biomedical Optical Imaging Technologies, 111–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28391-8_4.

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Tavares, João Manuel R. S., and Paulo Rui Fernandes. "Correction to: New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering." In Lecture Notes in Computational Vision and Biomechanics, C1. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23073-9_11.

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Liu, Jianfei, Q. Jackie Wu, Fang-Fang Yin, John P. Kirkpatrick, Alvin Cabrera, and Yaorong Ge. "An Active Optical Flow Model for Dose Prediction in Spinal SBRT Plans." In Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, 27–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14148-0_3.

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Sevick-Muraca, Eva M. "[31] Computations of time-dependent photon migration for biomedical optical imaging." In Part B: Numerical Computer Methods, 748–81. Elsevier, 1994. http://dx.doi.org/10.1016/s0076-6879(94)40070-9.

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"Medical Imaging Instrumentation and Techniques." In Computational Optical Biomedical Spectroscopy and Imaging, 381–408. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-17.

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"Developing a Comprehensive Taxonomy for Human Cell Types." In Computational Optical Biomedical Spectroscopy and Imaging, 143–72. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-10.

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"Functional Near-Infrared Spectroscopy and Its Applications in Neurosciences." In Computational Optical Biomedical Spectroscopy and Imaging, 173–94. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-11.

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"Computer-Aided Diagnosis of Interstitial Lung Diseases Based on Computed Tomography Image Analysis." In Computational Optical Biomedical Spectroscopy and Imaging, 195–220. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-12.

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"Induced Optical Natural Fluorescence Spectroscopy for Giardia lamblia Cysts." In Computational Optical Biomedical Spectroscopy and Imaging, 221–58. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-13.

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"Strong Interaction between Nanophotonic Structures for Their Applications on Optical Biomedical Spectroscopy and Imaging." In Computational Optical Biomedical Spectroscopy and Imaging, 259–80. CRC Press, 2015. http://dx.doi.org/10.1201/b18024-14.

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Conference papers on the topic "Computational methods in biomedical optical imaging"

1

Ripoll, Jorge, Vasilis Ntziachristos, and Eleftherios N. Economou. "Experimental demonstration of a fast analytical method for modeling photon propagation in diffusive media with arbitrary geometry." In European Conference on Biomedical Optics. Washington, D.C.: Optica Publishing Group, 2001. http://dx.doi.org/10.1364/ecbo.2001.4431_233.

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Quantitative measurements of diffusive media, in spectroscopic or imaging mode, rely on the generation of appropriate forward solutions, independently on the inversion scheme employed. For complicated boundaries, the use of numerical methods is usually pursued due to implementation simplicity, but this results in great computational needs. Even though some analytical expressions are available, an analytical solution to the diffusion that deals with arbitrary volumes and boundaries is needed. We use here an analytical approximation, the Kirchhoff Approximation or the tangent-plane method, and put it to test with experimental data in a cylindrical geometry. We examine the experimental performance of the technique, as a function of the optical properties of the medium and demonstrate how it greatly speeds up the computation time when performing 3D reconstructions.
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Kim, Chang-Keun, Keong-Jin Lee, Dong-Choon Hwang, Seung-Cheol Kim, and Eun-Soo Kim. "IVR-based computational reconstruction method in three-dimensional integral imaging with non-uniform lens array." In Biomedical Optics. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/biomed.2008.jma1.

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Shin, Dong-Hak, and Hoon Yoo. "3D image quality enhancement in computational integral imaging system by additional use of an interpolation method." In Biomedical Optics. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/biomed.2008.jma5.

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Español, Malena I., Suren Jayasuriya, and Mohit Malu. "Multilevel Methods for Imaging Applications." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/cosi.2020.cth4c.1.

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Schultz, Christian. "The Potential of Optical Methods in Molecular Imaging." In Biomedical Topical Meeting. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/bio.2006.tub3.

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Paes, Stephane, Seon Young Ryu, Jihoon Na, Eun Seo Choi, and Byeong Ha Lee. "Application of iterative deconvolution methods for optical coherent imaging." In Biomedical Optics 2005, edited by Valery V. Tuchin, Joseph A. Izatt, and James G. Fujimoto. SPIE, 2005. http://dx.doi.org/10.1117/12.592876.

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Oliveri, Giacomo, and Toshifumi Moriyama. "Compressive Sensing Methods Applied to Inverse Imaging Problems." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2014. http://dx.doi.org/10.1364/cosi.2014.cw2c.3.

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Kaur, S., J. Gomez-Blanco, A. Khalifa, S. Adinarayanan, R. Sanchez-Garcia, D. Wrapp, J. S. McLellan, K. H. Bui, and J. Vargas. "Local methods to improve cryo-electron microcopy maps." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/cosi.2021.ctu4b.3.

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Johnson, Gregory E., Ash K. Macon, and Goran M. Rauker. "Computational imaging design tools and methods." In Optical Science and Technology, the SPIE 49th Annual Meeting, edited by Jose M. Sasian, R. John Koshel, Paul K. Manhart, and Richard C. Juergens. SPIE, 2004. http://dx.doi.org/10.1117/12.558068.

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Lepage, K., and S. Kraut. "Multitaper Methods for Spectrum Estimation with a Rotational Shear Interferometer." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2005. http://dx.doi.org/10.1364/cosi.2005.ctuc3.

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