3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images

Diciotti, Stefano; Picozzi, Giulia; Falchini, Massimo; Mascalchi, Mario; Villari, Natale; Valli, G.

doi:10.1109/titb.2007.899504

Cited by 120 publications

(69 citation statements)

References 37 publications

(57 reference statements)

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“…1 Pulmonary nodules based on internal texture: a solid, b part-solid, and c non-solid tive performance measures were presented. Diciotti et al [7] also developed a semi-automated segmentation technique using 3D region growing based on gray-level similarity and shape of objects. The initial segmentation results are provided to the operator to confirm each pulmonary structure picked by the algorithm as a correct detection or to discard it.…”

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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“…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%

“…[189,190], RG was adopted in this manner. There are more recent studies [174,[192][193][194] that have extended this approach as the main component of their overall segmentation algorithms. Dehmeshki et al [192] proposed an adaptive shericity-oriented contrast-based RG on the fuzzy connectivity map computed from the results of local adaptive thresholding segmentation.…”

Section: Region Growing (Rg) Is Another Classical Image Segmentatimentioning

confidence: 99%

“…Dehmeshki et al [192] proposed an adaptive shericity-oriented contrast-based RG on the fuzzy connectivity map computed from the results of local adaptive thresholding segmentation. Diciotti et al [193] presented a RG method with a fusion-segregation criteria using geodesic distances. Finally, Kubota et al [174,194] proposed a RG on an…”

Section: Region Growing (Rg) Is Another Classical Image Segmentatimentioning

confidence: 99%

“…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%

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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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ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

et al. 2019

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Purpose: The automated segmentation of each lung and trachea in CT scans is commonly taken as a solved problem. Indeed, existing approaches may easily fail in the presence of some abnormalities caused by a disease, trauma, or previous surgery. For robustness, we present ALTIS (implementation is available at http://lids.ic.unicamp.br/downloads)a fast automatic lung and trachea CT-image segmentation method that relies on image features and relative shape-and intensity-based characteristics less affected by most appearance variations of abnormal lungs and trachea. Methods: ALTIS consists of a sequence of image foresting transforms (IFTs) organized in three main steps: (a) lung-and-trachea extraction, (b) seed estimation inside background, trachea, left lung, and right lung, and (c) their delineation such that each object is defined by an optimum-path forest rooted at its internal seeds. We compare ALTIS with two methods based on shape models (SOSM-S and MALF), and one algorithm based on seeded region growing (PTK). Results:The experiments involve the highest number of scans found in literature -1255 scans, from multiple public data sets containing many anomalous cases, being only 50 normal scans used for training and 1205 scans used for testing the methods. Quantitative experiments are based on two metrics, DICE and ASSD. Furthermore, we also demonstrate the robustness of ALTIS in seed estimation. Considering the test set, the proposed method achieves an average DICE of 0.987 for both lungs and 0.898 for the trachea, whereas an average ASSD of 0.938 for the right lung, 0.856 for the left lung, and 1.316 for the trachea. These results indicate that ALTIS is statistically more accurate and considerably faster than the compared methods, being able to complete segmentation in a few seconds on modern PCs. Conclusion: ALTIS is the most effective and efficient choice among the compared methods to segment left lung, right lung, and trachea in anomalous CT scans for subsequent detection, segmentation, and quantitative analysis of abnormal structures in the lung parenchyma and pleural space.

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3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images

Cited by 120 publications

References 37 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.

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

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