Reconstruction of optical scanned images of inhomogeneities in biological tissues by Monte Carlo simulation

Jeeva, J. B.; Singh, Megha

doi:10.1016/j.compbiomed.2015.02.014

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…MC techniques are a subset of computational algorithms that utilize the procedure of repeated random sampling to obtain numerical estimations of unknown parameters. They assess the impact of risk and allow the modelling of critical situations where many random variables are engaged (Ghobadi et al, 2020;Hecquet et al, 2007;Jeeva & Singh, 2015;Precharattana et al, 2011). The uses of the method are exceptionally widespread and have led numerous innovative discoveries in the fields of finance, game theory, and physics (Liesenfeld & Richard, 2001).…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

Section: Monte Carlo Methodsmentioning

confidence: 99%

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Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Section: Monte Carlo Methodsmentioning

confidence: 99%

Section: Monte Carlo Methodsmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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“…Optical tomography is a versatile technique for imaging of soft thick tissues such as human breast, whereas, the reflectance measurement could provide the images of thin as well as thick surface tissues. Theoretically (22) and experimentally (2) this has been shown that for normal incident photons the backscattered ones are detected at various locations of tissue surface. The photons detected close to entry point emerge from tissue structure closer to the surface (23), whereas, away from the entry point emerges from the deeper tissue structure (4).…”

Section: Introductionmentioning

confidence: 98%

Optical Scanning System for Imaging of Heterogeneity in Biological Tissues

Christopherjames

Singh³

2022

ECS Trans.

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A non-invasive hand-held optical probe for imaging of biological tissues consists of nine units. Each unit is equipped with one red LED and three photodetectors placed at distances 7, 12, and 17 mm from the source along the x-axis. For testing, three phantoms of paraffin wax are embedded with solid inhomogeneity placed at various depths. Human biological tissues consisting of right index finger, four fingers of the same hand, and a part of the forearm below elbow joint are used. The data on optical backscattering are collected by moving the probe on the surface of phantom/tissue and after processing the respective images are obtained. Three images of the index finger show the tissue structural variation in various layers, which are in agreement with anatomical details and radiograph of the finger. The tissue details of four fingers are in agreement with that of individual finger. Similarly, the three images of the forearm show the structural variation. These observations suggest that the present technique may be used to detect tissue compositional changes below the skin.

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“…Imaging with randomly scattered light is a significant challenge with a pressing need in non-invasive biomedical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the more significant applications is a non-invasive functional imaging technique called diffuse optical tomography (DOT), which uses near-infrared (NIR) light to map in 3D the optical characteristics of tissue by penetrating it deeply [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

2023

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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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Reconstruction of optical scanned images of inhomogeneities in biological tissues by Monte Carlo simulation

Cited by 13 publications

References 39 publications

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Optical Scanning System for Imaging of Heterogeneity in Biological Tissues

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

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