Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images

Choi, Wookjin; Choi, Tae‐Sun

doi:10.1016/j.ins.2012.05.008

Cited by 100 publications

(57 citation statements)

References 27 publications

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“…The LIDC radiologists annotations include freehand outlines of nodules ≥ 3 mm in diameter on each CT slice in which the nodules are visible, along with the subjective ratings on a five-or six-point scale of the following pathologic features: calcification, internal structure, subtlety, lobulation, margins, sphericity, malignancy, texture, and spiculation. The annotations also include a single mark (an approximate centroid) of nodules ≤ 3 mm in diameter as well as non-nodules ≥ 3 mm [63,62,69].…”

Section: Acquisition Of Datamentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Acquisition Of Datamentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

View full text Add to dashboard Cite

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“…The research work conducted by Choi et al [8], is closely related to this research work in which automatic detection of lung nodules is performed by using multiple intensity thresholds and rule-based pruning based on shapebased features of the nodule candidates. However, our work has some major differences.…”

Section: Methodsmentioning

confidence: 99%

“…The study by Brown et al [7] involved intensity thresholding, region growing and mathematical morphology to identify regions of interest. Similarly, the technique by Choi et al [8] uses optimal multiple thresholding and rule-based pruning that uses local shape features to extract potential nodules from the lung region. Upon analysis, it was observed that the algorithm provides good performance in the detection of high-contrast, well shaped nodules but has low sensitivity for irregularly shaped non-solid nodules.…”

Section: Introductionmentioning

confidence: 99%

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Jahangir¹,

Imran²,

Abbas³

et al. 2016

ijacsa

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Abstract-This paper proposed a computer-aided detection (CAD) system to automatically detect pulmonary nodules from thoracic computed tomography (CT) images. Automatically detect pulmonary nodules is a difficult job because of the large deviation in size, shape, location and density of nodules. The proposed CAD scheme applies multiple 3D disk-shaped laplacian filters to enhance the shape of spherical regions. Optimal multiple thresholding and 3D distance mapping is used to extract regions of interest and separate nodules. Finally, rule-based pruning removes easily dismissible false positive structures. The proposed system provides an overall nodule detection rate of 80% with an average of 12.2 false positives per scan. The experimental results reveals that the proposed CAD can attain a comparatively high performance.

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“…In the last few years, GP has been extensively used both in Industry and Academia (Arcuri & Yao, 2010;Chan, Kwong, & Fogarty, 2010;Choi & Choi, 2012;dos Santos, Ferreira, Torres, Gonçalves, & Lamparelli, 2011;Koza, Streeter, & Keane, 2008;Moreno-Torres, Llorá, Goldberg, & Bhargava, 2013;Ravisankar, Ravi, & Bose, 2010;Trujillo, Legrand, Olague, & Lévy-Véhel, 2012;Yeun, Suh, & Yang, 2000;Wongseree, Chaiyaratana, Vichittumaros, Winichagoon, & Fucharoen, 2007) and it has produced a wide set of results that have been defined human-competitive (Koza, 2010). While these results have demonstrated the appropriateness of GP in tackling real-life problems, research has recently focused on developing new variants of GP in order to further improve its performance.…”

Section: Geometric Semantic Operatorsmentioning

confidence: 99%