Lung nodule classification in frequency domain using support vector machines

Orozco, Hiram Madero; Villegas, Osslan Osiris Vergara; Maynez, Leticia Ortega; Sánchez, Vianey Guadalupe Cruz; Domínguez, Humberto de Jesús Ochoa

doi:10.1109/isspa.2012.6310676

Cited by 27 publications

(12 citation statements)

References 7 publications

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“…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%

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: Authorsmentioning

confidence: 99%

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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“…Finally, the results were compared with some of the latest similar researches on the subject as shown in Table 1 . In this table, the highest sensitivity is 96.15 in,[ 29 ] which is better than 90.1 of our algorithm, but our specificity is 92.8, better than 52.17 in. [ 29 ] This result shows that, compared with existing algorithms, our method performs at a similar or better level.…”

Section: Resultsmentioning

confidence: 99%

Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features

et al. 2017

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Background:Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules.Methods:In this paper, a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm segments two lung lobes to increase processing speed and accuracy. Second, template-matching is applied to detect the suspicious nodule candidates, including both nodules and some blood vessels. Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical features in three steps. In these steps, the size, long and short diameters and sphericity are used to decrease the false-positive rate.Results:In the first step, some vessels that are parallel to CT cross-plane are identified. In the second step, oblique vessels are detected using shift of center of gravity in two successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps, vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in this work.Conclusions:Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable in comparison to other related works.

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“…Classification in frequency domain using SVM classifier was performed by [43] [66]. [42] used SVM and weighted modifies Mahalanobis distance measure to nodule classification.…”

Section: Svm Based Classificationmentioning

confidence: 99%

Techniques for Detection of Solitary Pulmonary Nodules in Human Lung and Their Classifications -A Survey

Bhavanishankar¹,

M.V²

2015

IJCI

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Lung cancer is proving to be a catastrophic threat to the mankind and is main cause of deaths among other cancer related casualties. The presence of solitary pulmonary nodules in human lungs in the form of benign or malignant determines the gravity of lung ailment. This survey focusses on different techniques used to detect and classify the lung nodules which in turn will assist the domain experts for better diagnosis. Among many imaging modalities Computed Tomography (CT) being the most sought after because of its high resolution, isotropic acquisition which helps in locating the lung lesions. Since the volume of the CT scans are very large, Computer Aided Detection/Diagnosis (CAD/x) has more advantages in addition to manual interpretation with respect to speed and accuracy. This paper attempts to summarize various methods that have been proposed by several authors over the years of their research.

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Lung nodule classification in frequency domain using support vector machines

Cited by 27 publications

References 7 publications

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

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

Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features

Techniques for Detection of Solitary Pulmonary Nodules in Human Lung and Their Classifications -A Survey

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