Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis

Diciotti, Stefano; Lombardo, Simone; Falchini, Massimo; Picozzi, Giulia; Mascalchi, Mario

doi:10.1109/tbme.2011.2167621

Cited by 112 publications

(96 citation statements)

References 27 publications

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“…Diciotti et al [6] developed an automated segmentation refinement of small juxta-vascular solid nodules, based on 3D local shape analysis without any user interaction. The correction procedure refines an initial nodule segmentation in order to separate possible vessels from the nodule.…”

Section: Reported Work On Segmentation Of Pulmonary Nodules Segmentamentioning

confidence: 99%

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The wellcircumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/partsolid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

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Section: Reported Work On Segmentation Of Pulmonary Nodules Segmentamentioning

confidence: 99%

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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“…More complex morphological filtering based on iterative [95,248] and successive [190] combinations of these basic operators, convex hull operations [174,190], and 3D moment analysis [214] have also been adopted as a post segmentation refinement method. Geometric/shape-constrained segmentation is another popular approach in this context [192,193,207,222,249]. This approach integrates shape-based prior information within the segmentation process in order to bias the results toward a spherical/nodular shape and suppress elongated non-target structures attached to the target.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

“…This approach integrates shape-based prior information within the segmentation process in order to bias the results toward a spherical/nodular shape and suppress elongated non-target structures attached to the target. Gaussian model fitting [207], eigen analysis of the Hessian ma-trix [212,213], sphericity oriented region growing [192], geodesics distance constraints between connected-components [249], and a steepest-ascent test [174] are some examples of this type of geometric-constraint approaches.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

“…In order to improve their usability, such error correction process can be automated with an automatic detection of nodule types or of segmentation failures. Such nodule type-specific automatic error correction has been presented for juxtavascular [249] and juxtapleaural cases [191]. Semi-automatic error correction is of interest in its clinical context.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

“…One common approach is based on subjective judgment of segmentation results by visual inspection of expert observers. In this approach, the observer(s) classifies each case as success or failure, then the rate of successfully segmentation is reported [95,119,190,192,193,201,207,218,222,247,249].…”

Section: Validationmentioning

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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Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach

Chilakala

Kishore

2020

Int J Imaging Syst Tech

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In this manuscript, a new approach using deep belief network (DBN) along opposition-based hybrid grasshopper and honey bee optimization algorithm for lung cancer classification is proposed. Chest computed tomography (CT) is commonly used to diagnosis the lung tumors. Initially, the image quality is improved by preprocessing techniques, and then the features like texture, color and shape are extracted. Several functions have been originating from the second order first gray level statistics like modified angles, high-order algebraic time invariant, Gaussian defining properties and new spectral power metrics. Recently, a local binary pattern geometric characteristic descriptor has been demonstrated in the extraction and classification of pulmonary nodules. Furthermore, some functionality is stripped away for comparison purposes. Dimension reduction is significant for the implementation of algorithms in machine learning. The other special approaches can be implemented to determine the functionalities due to vast set of features. To remove the unused and obsolete features between the feature selection approaches, the multivariate approach like local tangent space alignment is used. Finally, DBN is used to categorize pulmonary CT images as malignant or benign, and is calibrated to the detection of lung cancer classification by the opposition-based hybrid grasshopper and honey bee optimization algorithm. The Lung Image Database Consortium including Image Resources Initiative database are analyzed the network services, and the experimental results shows that the DBN network represents 97.52% accuracy, 96% sensitivity and 94.58% specificity. K E Y W O R D Scomputed tomography (CT), deep belief networks (DBN), grasshopper and honey bee (GHHB) optimization algorithm, local binary pattern (LBP), local tangent space alignment (LTSA)

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Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis

Cited by 112 publications

References 27 publications

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

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

Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach

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