Optimal estimation of contour properties by cross-validated regularization

Shahraray, Behzad; Anderson, David J.

doi:10.1109/34.24794

Cited by 75 publications

(25 citation statements)

References 26 publications

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“…These two types of variability have to be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Ferreira

Santos

Silva

et al. 2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

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Quantitative evaluation of the performance of segmentation algorithms on medical images is crucial before their clinical use can be considered. We have quantitatively compared the contours obtained by a pulmonary segmentation algorithm to contours manually-drawn by six expert imaiologists on the same set of images, since the ground truth is unknown.Two types of variability (inter-observer and intra-observer) should be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed. This paper describes the quantitative evaluation of the performance of our segmentation algorithm using several figures of merit, exploratory and multivariate data analysis and non parametric tests, based on the assessment of the inter-observer variability of six expert imagiologists from three different hospitals and the intra-observer variability of two expert imagiologists from the same hospital.As an overall result of this comparison we were able to claim that the consistency and accuracy of our pulmonary segmentation algorithm is adequate for most of the quantitative requirements mentioned by the imagiologists. We also believe that the methodology used to evaluate the performance of our algorithm is general enough to be applicable to many other segmentation problems on medical images.

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“…These two types of variability have to be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Ferreira

Santos

Silva

et al. 2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

View full text Add to dashboard Cite

show abstract

“…To do this, we must first find an analytic function that best fits the discrete variance data. This in turn requires the use of an interpolation method such as polynomial-based interpolation, cubic spline and smoothing spline [35]- [39].…”

Section: Estimating H V´τ µ From Traffic Tracesmentioning

confidence: 99%

Describing Network Traffic Using the Index of Variability

Lazarou

Baca

Frost

et al. 2009

IEEE/ACM Trans. Networking

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Abstract-Commonly used measures of traffic burstiness do not capture the fluctuation of traffic variability over the entire range of time scales. In this paper, we present a measure of variability, called the Index of Variability (H v´τ µ), that fully and accurately captures the degree of variability (burstiness) of a typical network traffic process at each time scale and is analytically tractable for many traffic models. As an illustration, we derive the closed-form expression of H v´τ µ for two traditional traffic models and generate a variety of 2D and 3D Index of Variability curves. These curves demonstrate that the Index of Variability is a mathematically rigorous measure which can be used to fully characterize the complexities of the network traffic variability over all time scales. We then introduce a practical method for estimating the Index of Variability curve from a given traffic trace. Experimental results are presented which demonstrate the robustness of the method applied to the estimation of the Index of Variability curves from 12 NLANR network traffic long traces.

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“…These results confirm that M3 significantly maintains its template effectively and reduces the number of needless updates. We also conducted the leave-one-out cross validation test [7][8] for further assessment of the adaptive update method. The group of 12 persons is divided into 11 persons for training and one individual for evaluation respectively.…”

Section: Effectiveness Of the Adaptive Updatementioning

confidence: 99%

Arm Swing Identification Method with Template Update for Long Term Stability

Matsuo

Okumura

Hashimoto

et al.

Advances in Biometrics

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Abstract. This paper proposes a novel method for biometric identification, based on arm swing motions with a template update in order to improve long term stability. In our previous work, we studied arm swing identification and proposed a basic method to realize a personal identification function on mobile terminals. The method compares the acceleration signals of arm swing motion as individual characteristics, with the tolerant similarity measurement between two arm swing motions via DP-matching, which enables users to unlock a mobile terminal simply by swinging it. However, the method has a problem with long term stability. In other words, the arm swing motions of identical individuals tend to fluctuate among every trial. Furthermore, the difference between the enrolled and trial motions increases over time. Therefore in this paper, we propose an update approach to the enrollment template for DPmatching to solve this problem. We employ an efficient adaptive update method using a minimum route determination algorithm in DP-matching. Identification experiments involving 12 persons over 6 weeks confirm the proposed method achieves a superior equal error rate of 4.0% than the conventional method, which has an equal error rate of 14.7%.

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Optimal estimation of contour properties by cross-validated regularization

Cited by 75 publications

References 26 publications

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Describing Network Traffic Using the Index of Variability

Arm Swing Identification Method with Template Update for Long Term Stability

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