&lt;title&gt;Map-Matching Techniques For Use With Multispectral/Multitemporal Data&lt;/title&gt;

Kuglin, C. D.; Eppler, W. G.

doi:10.1117/12.959141

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…We investigated 3 types of imaging features on the basis of gray-level co-occurrence matrices, 18 wavelet decompositions, 19 and Laws features. 20 To meaningfully characterize tumors, all imaging features were further processed to be rotationally invariant (without preference to any particular spatial direction). The calculation of all imaging features was performed using IBEX software.…”

Section: Methodsmentioning

confidence: 99%

Use CT Imaging to Predict the Short-Term Outcome of Concurrent Chemoradiotherapy in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma

Cao

Wang

et al. 2019

Dose-Response

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Objective: To extract the computed tomography (CT) imaging features of the primary lesions in patients with advanced esophageal squamous cell carcinoma (ESCC) and to study whether these imaging features can predict the short-term outcome after concurrent chemoradiotherapy (CCRT). Methods: From January 2014 to December 2015, a total of 49 patients with locally advanced ESCC who underwent CCRT were analyzed retrospectively. They were randomly categorized into the training and validation groups. Collection of CT imaging of patients before and intermediate stage undergoing radiotherapy. The correlations between imaging characteristics and short-term outcome were analyzed. The accuracy of cutoff value was verified by imaging characteristics of patients in validation group. Result: There were 38 patients in the training group and 11 patients in the validation group. 13 patients in the training group were classified as responders and 25 patients as nonresponders. According to the CT imaging before radiotherapy, there are no significant differences between responders and nonresponders. According to the CT imaging in the middle stage of radiotherapy, responders showed significantly higher Roundness than nonresponders ( P = .004, 95% confidence interval [CI] = 0.0419-0.212). The areas under the ROC curves for the ability to predict significantly tumor response were 0.768 for Roundness ( P = .001, 95% CI = 0.603-0.889). The cutoff value of Roundness is 0.3099. Roundness showed no significant associations with survival parameters. Conclusions: Computed tomography imaging in the middle stage of radiotherapy can predict the short-term outcome of concurrent chemoradiotherapy for patients with locally advanced ESCC but have no predictive effect on the total survival time.

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Section: Methodsmentioning

confidence: 99%

Use CT Imaging to Predict the Short-Term Outcome of Concurrent Chemoradiotherapy in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma

Cao

Wang

et al. 2019

Dose-Response

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“…The errors clearly separate into two groups: 1) small errors associated with the estimation of the correct matchpoint and; 2) large errors or "false fixes" which result when the matchpoint amplitude is exceeded by one or more remote peaks. Because of the complex temporal contrast variations which occur in this scene, algorithm performance is directly related to algorithm flexibility.…”

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confidence: 98%

<title>Histogram-Based Algorithms For Scene Matching</title>

Kuglin¹

1982

SPIE Proceedings

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The two -dimensional joint histogram or co-occurrence matrix of two images provides a convenient vehicle for the design of scene matching algorithms which effectively incorporate information about expected variations between reference and sensor -derived data. Thus, specified histogram pairings, orderings, or degrees of clustering can be rewarded as appropriate by the matching algorithm. In particular, intensity-independent algorithms can be designed which are invariant under an arbitrary permutation of labels in one or both images to be matched and, as a result, they are insensitive to contrast reversals. Such algorithms can utilize synthetic reference maps constructed by assigning arbitrary labels to designated regions, thus eliminating the need for material identification and sensor response prediction. Two different types of intensityindependent algorithms are described and their performance is compared to that of conventional matching algorithms using a set of infrared images acquired over a 24 -hour period.

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Sensitivity analysis of similarity metrices for image matching

Devarajan

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

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This paper presents simulation results on the performance of similarity metrices for image matching. Specifically, matching and determining the transformation between slightly rotated images are addressed. The use of a correlation function and an error function as similarity metrices is reexamined when there is a coupling between translation and rotation. Sensitivity of these two metrices in the above context is compared. A hybrid iterative strategy is proposed based on the sensitivity analysis to enhance the matching accuracy .

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<title>Map-Matching Techniques For Use With Multispectral/Multitemporal Data</title>

Cited by 4 publications

References 2 publications

Use CT Imaging to Predict the Short-Term Outcome of Concurrent Chemoradiotherapy in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma

Use CT Imaging to Predict the Short-Term Outcome of Concurrent Chemoradiotherapy in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma

<title>Histogram-Based Algorithms For Scene Matching</title>

Sensitivity analysis of similarity metrices for image matching

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