&lt;title&gt;Detectability of radiological images: the influence of anatomical noise&lt;/title&gt;

Bochud, François; Verdun, Francis R.; Hessler, C; Valley, J.-F.

doi:10.1117/12.206845

Cited by 27 publications

(25 citation statements)

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“…It has been suggested that various classes of images, including mammograms, have power spectra of the form 1/f ␣ . [22][23][24][25] For mammograms, estimated values of ␣ in the range of 1.5 to 2 have been reported. 24,25 A power-law spectrum exponent between 1.5 and 2 indicates that mammograms are not fractals.…”

Section: A Two-component Model Synthesis Frameworkmentioning

confidence: 99%

“…Specifically, two studies demonstrate that two sets of images with equal power spectra, yet having Fourier spectra that differ in phase, yield different detectability performance and thus require different predictive mathematical models. 22,24 An ensemble of images with the same power spectrum as that of another ensemble of images but with a Fourier spectrum of random phase was obtained by filtering various realizations of white noise with the desired power spectrum.…”

Section: A Two-component Model Synthesis Frameworkmentioning

confidence: 99%

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Synthesis of textured complex backgrounds

Rolland

Goon

1998

Opt. Eng

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The statistical characterization of realistic, complex backgrounds where targets may be embedded is essential in the optimization of methods for target acquisition. A modeling framework for complex backgrounds that yields perceptually realistic images may provide a path towards such essential characterizations. To this end, we developed a framework for the synthesis of statistical textured backgrounds. The results of the syntheses of three rock-like structures and that of grass indicate some of the capabilities of the framework. We further extended the methods to synthesize biological tissue samples which present two forms of background complexity: a slowly, spatially varying meanbackground and a residual texture image. The extended framework allows synthesis of each component independently. A mathematical phantom for modeling inhomogeneous backgrounds is then proposed. First and second order statistics of various textures are then presented and a measure of distance between two images is proposed. Finally we discuss how such a framework may lead to effective statistical descriptions of such complex backgrounds for target acquisition. © 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)01807-8] Subject terms: textured backgrounds; complex backgrounds; texture synthesis; first and second order statistics; detection.Paper TAM-18

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Section: A Two-component Model Synthesis Frameworkmentioning

confidence: 99%

Section: A Two-component Model Synthesis Frameworkmentioning

confidence: 99%

Synthesis of textured complex backgrounds

Rolland

Goon

1998

Opt. Eng

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“…In this study, we first registered the detected nodule candidates in the PA image with the nodule candidates in the images at 3 and À3 , and then registered the nodule candidates in the two images at 63 .…”

Section: Iif Registration Of the Nodule Candidatesmentioning

confidence: 99%

“…Therefore, the sensitivity and specificity for screening lung cancer in chest radiography are very low. 3,4 As the computed tomography (CT) has advanced rapidly, more and more hospitals utilize CT to screen lung cancers. The National Lung Screening Trail, 5 a clinical study sponsored by the National Cancer Institute, showed that 20% fewer lung cancer deaths have been observed among those who were screened with low-dose spiral CT than those with chest radiography.…”

Section: Introductionmentioning

confidence: 99%

A computerized scheme for lung nodule detection in multiprojection chest radiography

Guo

Boyce

et al. 2012

Medical Physics

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Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.

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“…A useful extension of Rolland's model is the clustered lumpy background devised by Bochud et al 25 to represent the texture in mammograms. In that work, a cluster of identical blobs forms a "superblob", and the final model is a superposition of superblobs: (6.2) Examples are shown in Fig.…”

Section: Generation Of Lumpy and Clustered Lumpy Backgroundsmentioning

confidence: 99%

Statistical characterization of radiological images: basic principles and recent progress

Barrett

Myers

2007

SPIE Proceedings

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This paper surveys our current understanding of the statistical properties of radiological images and their effect on image quality. Attention is given to statistical descriptions needed to compute the performance of ideal or ideal-linear observers on detection and estimation tasks. The effects of measurement noise, random objects and random imaging system are analyzed by nested conditional averaging, leading to a three-term expansion of the data covariance matrix. Characteristic functionals are introduced to account for the object statistics, and it is shown how they can be used to compute the image statistics.

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<title>Detectability of radiological images: the influence of anatomical noise</title>

Cited by 27 publications

References 0 publications

Synthesis of textured complex backgrounds

Synthesis of textured complex backgrounds

A computerized scheme for lung nodule detection in multiprojection chest radiography

Statistical characterization of radiological images: basic principles and recent progress

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