A multiscale Laplacian of Gaussian filtering approach to automated pulmonary nodule detection from whole-lung low-dose CT scans

Fotin, Sergei V.; Reeves, Anthony P.; Biancardi, Alberto; Yankelevitz, David F.; Henschke, Claudia I.

doi:10.1117/12.811420

Cited by 17 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did not, as was done for example in Fotin et al (2009), specifically add cases with larger nodules. The ANODE09 set can be considered representative of findings among asymptomatic heavy smokers.…”

Section: Datamentioning

confidence: 95%

“…A large number of systems for nodule detection have been proposed in the literature Arimura et al, 2004;Armato et al, 2001Armato et al, , 2002Bae et al, 2005;Brown et al, 2003;Dehmeshki et al, 2007;Enquobahrie et al, 2007;Farag et al, 2004;Ge et al, 2005;Ko and Betke, 2001;Kung et al, 2004;Lee et al, 2001;Matsumoto et al, 2006;McCulloch et al, 2004;Mendonça et al, 2007;Murphy et al, 2007;Novak et al, 2004;Osman et al, 2007;Paik et al, 2004;Retico et al, 2008;Suzuki et al, 2003;Wei et al, 2002;Wiemker et al, 2005Wiemker et al, , 2002Ye et al, 2007;Zhang et al, 2007;Zhao et al, 2003;Fotin et al, 2009). In addition, several commercial systems for nodule detection are available and many workstations that radiologists routinely use to interpret CT scans provide on-board nodule detection capabilities.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Ginneken

Armato

Hoop

et al. 2010

Medical Image Analysis

272

161

View full text Add to dashboard Cite

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

show abstract

Section: Datamentioning

confidence: 95%

Section: Introductionmentioning

confidence: 98%

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Ginneken

Armato

Hoop

et al. 2010

Medical Image Analysis

272

161

View full text Add to dashboard Cite

show abstract

“…For example, in [21] Gaussian has been used for locate pulmonary nodules, and other studies have shown that the characteristic scale of a Laplacian of Gaussian (LoG) agreed well with radiologistsąŕ estimates of nodule size [22,23]. Kong et al [24] propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images,Miao et al [25] used rank order LoG filter for interest point detection, and Shi et al [2] proposed a dot enhancement filter by combining Hessian matrix and LoG filter.…”

Section: Related Workmentioning

confidence: 81%

A joint ROI extraction filter for computer aided lung nodule detection

Shi

Zhao

et al. 2015

BME

View full text Add to dashboard Cite

Abstract. Extraction of regions of interest plays an important rule in computer aided lung nodules detection. However, because of the complex background and structure, accurate and robust extraction of ROIs in medical image still remains a problem. Aim at this problem, a two-stage operations joint filter: Hessian-LoB, is proposed. The first stage is blobs (which being taken as candidate ROIs) detection and the second stage is ROIs extraction. In the first stage, the derivatives of a Hessian matrix at multiple scales are convolved with input images to localize blobs. Then in the second stage, Laplacian of bilateral filter (LoB) is convolved with the detected blobs to extract the final ROIs. Experiments show that the proposed filter can deal with images with noise and low brightness contrast, and is effectively in ROI extraction for lung nodule detection.

show abstract

“…This approach previously produced good results for nodule candidate generation in pulmonary nodule detection task [10]. For nodule location and size estimation, the algorithm generates nodule candidates in a small region of interest around the nodule, and the best candidate is selected through a rule-based method.…”

Section: A Scale-normalized Log Filtering Methodsmentioning

confidence: 99%

“…Gaussian fitting has been used to locate and size pulmonary nodules [8], and other studies have shown that the characteristic scale of a Laplacian of Gaussian (LoG) agreed well with radiologists' estimates of nodule size [9], [10]. In this work, we present a fully automated method for estimating the size and location of a nodule using a multiscale LoG filtering approach.…”

Section: Introductionmentioning

confidence: 99%

Automated nodule location and size estimation using a multi-scale laplacian of Gaussian filtering approach

Jirapatnakul

Fotin

Reeves

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Abstract-Estimation of nodule location and size is an important pre-processing step in some nodule segmentation algorithms to determine the size and location of the region of interest. Ideally, such estimation methods will consistently find the same nodule location irregardless of where the the seed point (provided either manually or by a nodule detection algorithm) is placed relative to the "true" center of the nodule, and the size should be a reasonable estimate of the true nodule size. We developed a method that estimates nodule location and size using multi-scale Laplacian of Gaussian (LoG) filtering. Nodule candidates near a given seed point are found by searching for blob-like regions with high filter response. The candidates are then pruned according to filter response and location, and the remaining candidates are sorted by size and the largest candidate selected. This method was compared to a previously published template-based method. The methods were evaluated on the basis of stability of the estimated nodule location to changes in the initial seed point and how well the size estimates agreed with volumes determined by a semi-automated nodule segmentation method. The LoG method exhibited better stability to changes in the seed point, with 93% of nodules having the same estimated location even when the seed point was altered, compared to only 52% of nodules for the templatebased method. Both methods also showed good agreement with sizes determined by a nodule segmentation method, with an average relative size difference of 5% and -5% for the LoG and template-based methods respectively.

show abstract

A multiscale Laplacian of Gaussian filtering approach to automated pulmonary nodule detection from whole-lung low-dose CT scans

Cited by 17 publications

References 29 publications

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

A joint ROI extraction filter for computer aided lung nodule detection

Automated nodule location and size estimation using a multi-scale laplacian of Gaussian filtering approach

Contact Info

Product

Resources

About