Frequency-Domain Optical Tomographic Imaging of Arthritic Finger Joints

Hielscher, Andreas H.; Kim, Hyun Keol; Montejo, Ludguier D.; Blaschke, Sabine; Netz, Uwe J.; Zwaka, PA; Illing, Gerd; Müller, Gerhard A.; Beuthan, J.

doi:10.1109/tmi.2011.2135374

Cited by 56 publications

(71 citation statements)

References 42 publications

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“…3 While substantial advances have been made in building clinically useful instruments and developing image reconstruction algorithms, much less effort has been spent on developing image analysis tools that can aid in quantifying or detecting the presence of diseased tissue.…”

Section: Overviewmentioning

confidence: 99%

“…We apply this approach to images of finger proximal interphalangeal (PIP) joints obtained from 20 healthy volunteers and 33 subjects with RA. 3 In Part 1, we establish a framework for extracting features of interest from three-dimensional (3-D) DOT images. The statistical significance of each feature is evaluated with classical statistical methods, including Kruskal-Wallis analysis of variance (ANOVA), Dunn's test, and receiver-operator-characteristics (ROC) analysis.…”

Section: Overviewmentioning

confidence: 99%

“…3 Each subject was evaluated by a rheumatologist and diagnosed for RA according to guidelines set by the ACR. 26 The clinically dominant hand of each subject was imaged with ultrasound and low-field MRI.…”

Section: Clinical Datamentioning

confidence: 99%

“…22,23 More recently, we reported on the ability to accurately diagnose RA from frequency domain (FD) DOT images of PIP joints using multidimensional LDA. 3 In that study we introduced classification of PIP joints using both μ a and μ 0 s data. The study proved that classification with FD-DOT images (91% Se and 86% Sp) was significantly more accurate than classification with CW-DOT images (64% Se and 55% Sp).…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

et al. 2013

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Abstract. This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p < 0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Clinical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

et al. 2013

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“…Thus, a range of different applications have been devised and initially tested in clinics, such as optical mammography for the detection of breast lesions, [1][2][3] monitoring of neoadjuvant chemotherapy, 4 assessment of breast density as a cancer risk factor, 5 functional imaging of the brain, [6][7][8] monitoring of blood perfusion and oxygenation in the injured brain, 9 muscle oximetry, 10 studies of epilepsy, 11,12 and investigation of bone and joint pathologies, 13 just to cite same major examples.…”

Section: Introductionmentioning

confidence: 99%

Phantoms for diffuse optical imaging based on totally absorbing objects, part 1: basic concepts

et al. 2013

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Abstract. The design of inhomogeneous phantoms for diffuse optical imaging purposes using totally absorbing objects embedded in a diffusive medium is proposed and validated. From time-resolved and continuous-wave Monte Carlo simulations, it is shown that a given or desired perturbation strength caused by a realistic absorbing inhomogeneity of a certain absorption and volume can be approximately mimicked by a small totally absorbing object of a so-called equivalent black volume (equivalence relation). This concept can be useful in two ways. First, it can be exploited to design realistic inhomogeneous phantoms with different perturbation strengths simply using a set of black objects with different volumes. Further, it permits one to grade physiological or pathological changes on a reproducible scale of perturbation strengths given as equivalent black volumes, thus facilitating the performance assessment of clinical instruments. A set of plots and interpolating functions to derive the equivalent black volume corresponding to a given absorption change is provided. The application of the equivalent black volume concept for grading different optical perturbations is demonstrated for some examples.

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Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

Bose,

Srinivasan,

Surenderanath

2024

Int J Imaging Syst Tech

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Rheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x‐ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer‐independent computer‐aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed‐forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray‐level co‐occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate.

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Frequency-Domain Optical Tomographic Imaging of Arthritic Finger Joints

Cited by 56 publications

References 42 publications

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

Phantoms for diffuse optical imaging based on totally absorbing objects, part 1: basic concepts

Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

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