The ${LoG}$ Characteristic Scale: A Consistent Measurement of Lung Nodule Size in CT Imaging

Diciotti, Stefano; Lombardo, Simone; Coppini, Giuseppe; Grassi, L; Falchini, Massimo; Mascalchi, Mario

doi:10.1109/tmi.2009.2032542

Cited by 30 publications

(19 citation statements)

References 43 publications

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“…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: 78%

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

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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.

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Section: Introductionmentioning

confidence: 78%

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

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“…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: 99%

A joint ROI extraction filter for computer aided lung nodule detection

Shi

Zhao

et al. 2015

BME

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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.

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“…Technical approaches previously reported for volumetric lung nodule segmentation can be roughly classified into the following eleven categories: 1) thresholding [167,168,[183][184][185][186][187], 2) mathematical morphology [95,98,[188][189][190][191], 3) region growing [174,189,190,[192][193][194], 4) deformable model [195][196][197][198][199][200][201], 5) dynamic programming [202][203][204], 6) spherical/ellipsoidal model fitting [205][206][207][208][209] [220,221], and 11) watersheds [222]. Next, an overview of the technical approaches for lung nodule segmentation is given.…”

Section: Ct Nodules Segmentation Techniquesmentioning

confidence: 99%

“…Then the most stable scale that minimizes Jensen-Shannon divergence [230] computed over the varying scales determines the final outcome. In Diciotti et al [209], the nodule size was estimated by using the multi-scale Laplacian of Gaussian (LoG) fil-tering [231]. The characteristic scale defined over the LoG scale-space was adopted as the lesion's size estimate and as an initialization of their RG-based segmentation method [193].…”

Section: Deformable Model (Dm) Represents a Class Of Segmentation mentioning

confidence: 99%

“…The RAGF method [207] imposes the ellipsoidal constraints for handling juxtaposed cases. A similar approach with Laplacian of Gaussian filtering, imposing the spherical constraints of various sizes, has also been applied to detection and rough segmentation of nodules [208,209]. Recently, Kubota et al [174,194] have proposed a robust region growing method that successfully handles all types of nodules.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

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Computational methods for the analysis of functional 4D-CT chest images.

Soliman¹

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DEDICATIONThis dissertation is dedicated to my father's soul.iii ACKNOWLEDGMENTS In the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me. Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients.v The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decisionmaking. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification.This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today's clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered...

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The ${LoG}$ Characteristic Scale: A Consistent Measurement of Lung Nodule Size in CT Imaging

Cited by 30 publications

References 43 publications

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

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

A joint ROI extraction filter for computer aided lung nodule detection

Computational methods for the analysis of functional 4D-CT chest images.

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