Multi-level Ground Glass Nodule Detection and Segmentation in CT Lung Images

Tao, Yimo; Lü, Le; Dewan, M. Ali Akber; Chen, Albert Y.; Corso, Jason J.; Xuan, Jianhua; Salganicoff, Marcos; Krishnan, Arun V.

doi:10.1007/978-3-642-04271-3_87

Cited by 41 publications

(62 citation statements)

References 11 publications

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“…Their algorithm was tested on 10 non-solid nodules, and no quantitative segmentation results were reported. Tao et al [24] developed a segmentation technique based on the determination of voxel-based probability map followed by multiscale blob enhancement filter. The technique is applied on each voxel to obtain a volumetric blobness likelihood map.…”

Section: Segmentation Of Non-solid Pulmonary Nodulesmentioning

confidence: 99%

“…Most of the reported works focus on segmentation of solid nodules [5-7, 12, 14, 17, 19]. Few methods are reported on segmentation of non-solid nodules [24,25], and only one method is reported on segmentation of solid, part-solid, and non-solid nodules [13]. Hensckle et al [8] reported that part-solid and non-solid nodules have high risk of malignancy compared to solid nodules.…”

Section: Introductionmentioning

confidence: 99%

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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: Segmentation Of Non-solid Pulmonary Nodulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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“…The majority of existing work focuses on segmentation on CT images using various classification techniques [1,2,3]. Our method is partially motivated by these approaches.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference

Song

Cai

Eberl

et al. 2011

Lecture Notes in Computer Science

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Abstract. Positron emission tomography -computed tomography (PET-CT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a discriminative, multi-level learning and inference method to automatically detect the pathological contexts in the thoracic PET-CT images, i.e. the primary tumor and its spatial relationships within the lung and mediastinum, and disease in regional lymph nodes. The detection results can also be used as features to retrieve similar images with previous diagnosis from an imaging database as a reference set to aid physicians in PET-CT scan interpretation. Our evaluation with clinical data from lung cancer patients suggests our approach is highly accurate.

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“…Browder et al [214] also proposed a ML approach for three classes (solid nodule, nonsolid nodule, and parenchymal tissue), where a Gaussian model is used to define each distribution. In Tao et al [215], likelihoods are derived by GMMs over a subspace found by linear discriminant analysis of various intensity features, yielding probability maps. Final segmentation is given by thresholding the map with a shape-prior.…”

Section: G Probabilistic Classification (Pc) Is Another Popular Apprmentioning

confidence: 99%

“…Due to the clinical interests and technical challenge, many attempts have recently been made to propose segmentation solutions for this nodule subtype [119, 168, 174, 194, 200, 210-215, 218, 246]. Most common approach among them was the voxel-wise probabilistic classification in order to handle the subtle and irregular lesion appearances [119,[210][211][212][213][214][215]. In this approach, segmentation is performed by assigning each voxel with a nodule/background label according to its probabilistic decision rule derived from training data.…”

Section: Pet Segmentation Techniquesmentioning

confidence: 99%

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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Multi-level Ground Glass Nodule Detection and Segmentation in CT Lung Images

Cited by 41 publications

References 11 publications

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference

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

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