Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis

Arai, Kohei; Herdiyeni, Yeni; Okumura, Hiroshi

doi:10.14569/ijacsa.2012.030416

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…The local binary pattern on three orthogonal planes (LBP-TOP) was used for calculating a feature vector 11,18,29,30,31 . Naïve implementation of 2D LBP was represented as follows:…”

Section: Calculation Of a Feature Vectormentioning

confidence: 99%

“…The purpose of the current study was to develop and evaluate the CADx system, focusing on (i) usefulness of XGBoost and (ii) effectiveness of parameter optimization using random search and Bayesian optimization. Herein, a hand-crafted imaging feature, a variant of the local binary pattern (LBP) 11,18,[29][30][31] , was used for calculating a feature vector that is fed into a machine learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

et al. 2018

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We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

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“…The local binary pattern on three orthogonal planes (LBP-TOP) was used for calculating a feature vector 11,18,29,30,31 . Naïve implementation of 2D LBP was represented as follows:…”

Section: Calculation Of a Feature Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

et al. 2018

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“…While a fast growth rate usually indicates malignancy, the outcome requires sufficient time for the growth, more CT scans, and accurate image segmentation for the nodule volumes at two or more time points. Although the geometry related features from the CT images have played an important role for the differentiation task [13][14][15][16][17][18][19], texture features have gained great attention for lung CADx [20][21][22][23] in recent years. We summarize these recently reported image features and their performance levels in Appendix, where their used datasets are also included.…”

Section: Introductionmentioning

confidence: 99%

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

et al. 2014

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Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90% differentiation rate, the Haralick features achieved the highest AUC value of 92.70% at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.

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“…To get the appropriate results, these parameters are required to adjust in a specific order. In [12], a relative learning is presented for twodimensional and three-dimensional using LBP technique to diagnose lungs cancer from CT scan images. The technique was tested on a number of lungs CT images from -Japan Society of Computer Aided Diagnosis of Medical Images‖.…”

Section: Literature Reviewmentioning

confidence: 99%

Object’s Shape Recognition using Local Binary Patterns

Wasim¹,

Siddiqui²,

Aziz³

et al. 2017

ijacsa

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AbstractThis paper discusses the concept of object's shape identification using local binary pattern technique (LBP). Since LBP is computationally simple it has been utilized successfully for recognition of various objects. LBP which has the potential to be used in various identification related fields was applied on a number of different shaped objects, the process converted the given image in to 3x3 binary matrices and several rounds of computation yields the final decision parameter, which is known as merit function. This parameter was then exploited to uniquely identify the shape of different objects.

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Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis

Abstract: Keywords-lung cancer detection; local binary pattern; probabilistic neural network.

Cited by 17 publications

References 9 publications

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

Object’s Shape Recognition using Local Binary Patterns

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