Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Santos, Alberto Monteiro dos; Filho, Antônio Oséas de Carvalho; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

doi:10.1016/j.engappai.2014.07.007

Cited by 89 publications

(49 citation statements)

References 31 publications

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“…After this analysis, only 38 articles [24,4,18,22,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58] were identified as 3D automated algorithms to segment lung nodules in CT images, the target of this work.…”

Section: Work Selection Criteriamentioning

confidence: 99%

“…The radiologist may also suffer interference factors such as fatigue, Authors Computational technique(s) Choi and Choi [24], Santos et al [4], Chen et al [27] and Li and Doi [80] Hessian matrix based method El-Baz et al [22] and Le et al [81] Genetic algorithm template matching Cascio et al [26] Stable 3D mass-spring models Soltaninejad, Keshani and Tajeripour [28] k-Nearest Neighbors (k-NN) classifier and active contour Suiyuan and Junfeng [29] Thresholding Awai et al [82] Sieve filter Tanino et al [83] Variable n-quoit filter Riccardi et al [30] 3D fast radial transform Namin et al [32] and Murphy et al [84] Shape index Ozekes, Osman and Ucan [38] 3D template matching Ge et al [45] Adaptive weighted k-means clustering Yamada et al [85] and Kanazawa et al [86] Fuzzy clustering Mekada et al [51] Maximum distance inside a connected component Mao et al [87] Fragmentary window filtering Mendonça et al [88] Curvature tensor Paik et al [89] Statistical shape model Agam and Armato [90] Correlation-based enhancement filters Wang et al [25] and Armato III et al [91] Multiple gray-level thresholding Saita et al [92] 3D labeling method subjectivity of the analysis, images acquired with improper configuration of the equipment and noise. A detailed analysis of the LIDC-IDRI database can help us understand the difficulties encountered during this task.…”

Section: False Positive Reductionmentioning

confidence: 99%

“…Computational classifier Santos et al [4], Wang et al [25], Choi and Choi [24], Riccardi et al [30], Liu et al [31], Ozekes and Osman [37], Yang, Periaswamy and Wu [39] and Orozco et al [94] Support vector machines (SVM) El-Baz et al [22] Bayesian supervised Cascio et al [26], Ashwin et al [72], Lin et al [95] and Bellotti et al [96] Artificial neural networks (ANN)…”

Section: Authorsmentioning

confidence: 99%

“…Santos et al [4] created a methodology for automatic detection of small lung nodules (with diameters between 2 and 10 mm) using Gaussian mixture Badura and Pietka [18] developed a new approach to the segmentation of different types of lung nodules on lung CT images. The technique is based on two fields of computational intelligence: fuzzy connectivity (FC) and evolutionary computation.…”

Section: Selected Workmentioning

confidence: 99%

“…More recently, one of the hopes to change the scenario of late diagnosis has been conducted by monitoring programs with low-dose CT, particularly applied to risk groups such as smokers [4].…”

Section: Introductionmentioning

confidence: 99%

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

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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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: Work Selection Criteriamentioning

confidence: 99%

Section: False Positive Reductionmentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

Section: Selected Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

Jena

George

Ponraj

2021

Int J Imaging Syst Tech

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The shape and size of nodules are significant pointers of malignancy in cancer diagnosis. Moreover, considerable capture of nodules' structural data attained from computed tomography (CT) scans in computer-aided design is a confronting task. Various investigations deal with computationally deep ensemble approaches/convolutional neural network (CNN) models; however, sampling tumors based on multi-section-You Only Look Once (MS-YOLO) architecture, this anticipated model acquires nodules' multi-sections from various views and encodes nodule's information to data aggregation from diverse cross-sections through pooling layers. Subsequently, the features are utilized for the nodule classification task. This MS YOLO does not need any nodules' spatial annotation. However, it works directly over the cross-section acquired from enclosed nodule volume. This work has been analyzed using LUNA R16 dataset. It attains adequate performance with a mean value of 90.8% classification accuracy. Anticipated architecture is cast-off to choose cross-section determination of malignancy that helps in output interpretation. The proposed model shows better trade-off among previous methodologies. Simulation was carried out in MATLAB environment.

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A fully automatic segmentation algorithm for CT lung images based on random forest

2019

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Purpose Several negative factors, such as juxta‐pleural nodules, pulmonary vessels, and image noise, make accurately segmenting lungs from computed tomography (CT) images a complex task. We propose a novel hybrid automated algorithm in the paper based on random forest to deal with the issues. Our method aims to eliminate the effect of the factors and generate accurate segmentation of lungs from CT images. Methods Our algorithm consists of five main steps: image preprocessing, lung region extraction, trachea elimination, lung separation, and contour correction. A lung CT image is first preprocessed with a novel normal vector correlation‐based image denoising approach and decomposed into a group of multiscale subimages. A modified superpixel segmentation method is then performed on the first‐level subimage to generate a set of superpixels, and a random forest classifier is employed to segment the lungs by classifying the superpixels of each subimage‐based on the features extracted from them. The initial lung segmentation result is further refined through trachea elimination using an iterative thresholding approach, lung separation based on context information of image sequence, and contour correction with a corner detection technique. Results Our algorithm is tested on a set of CT images affected with interstitial lung diseases, and experiments show that the algorithm achieves high accuracy on lung segmentation with 0.9638 Jaccard’s index and 0.9867 Dice similarity coefficient, compared with ground truths. Additionally, our algorithm achieves an average 7.7% better Dice similarity coefficient than compared conventional lung segmentation methods and 1% better than Deep Learning. Conclusions Our algorithm can segment lungs from lung CT images with good performance in a fully automatic fashion, and it is of great assistance for lung disease detection in the computer‐aided detection system.

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Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Cited by 89 publications

References 31 publications

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

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

Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

A fully automatic segmentation algorithm for CT lung images based on random forest

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