Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model

Lin, Phen-Lan; Huang, Po-Whei; Lee, Cheng-Hsiung; Wu, Ming-Ting

doi:10.1016/j.patcog.2013.06.017

Cited by 56 publications

(41 citation statements)

References 26 publications

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“…Model-based approaches such as fractal analysis use mathematic models to derive textural properties from images [23][24][25][26]. Fractal dimension (FD) refers to self-repeating textures that repeat a pattern as one magnifies the feature.…”

Section: Overview Of Texture Analysis Techniquesmentioning

confidence: 99%

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

131

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Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

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Section: Overview Of Texture Analysis Techniquesmentioning

confidence: 99%

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

131

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“…Compared with 2D method, the addition of extra dimension dramatically increases the operational complexity and computational cost for processing the entire 3D nodule volume. Thus, to reduce both the computational cost and radiation dose, the study in this paper tries to distinguish between benign and malignant nodules by using a 2Dapproach for a single post-contrast CT scan [64]. www.ijarai.thesai.org…”

Section: ) 3d-based Approachesmentioning

confidence: 99%

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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Abstract-Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient's chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

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“…log(E(M /:,r ))=Hlog(i1J + K(constant) Since fractal dimension FD= 3-H, and higher FD indicates rougher texture [10]. Thus, lower H value implies higher fractal dimension, which in turn stands for higher degree of texture roughness.…”

Section: Gray-level Co-occurrence Matrixmentioning

confidence: 99%

“…An ROC curve depicts relative tradeoffs between benefits (true positives) and costs (false positives), where TPR and FPR are defined as [11]: TPR = TP /(TP + FN) (9) FPR = FP/(FP + TN) (10) in which TP is true positive, FN is false negative, FP is false positive and TN is true negative.…”

Section: Receiver Operating Characteristics Curve and Areamentioning

confidence: 99%

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

Huang

Lin

et al. 2014

2014 International Conference on Machine Learning and Cybernetics

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Automatic detection of alveolar bone-loss areas in dental periapical radiographs is a very challenging task because of the common uneven illumination problem of dental radiographs and complex topology of bone-loss areas. In this paper, we propose an effective automatic detection method ABL-Imm, which uses weighted average of both the intensity and the texture measured by the H-value of fractal Brownian motions (mm) model. The weights are trained with receiver operating characteristics (ROC) curve based on leave-one-out cross validation mechanism and the principle of the minimum area under the ROC curve (AUC). Through the weighted average of both features, radiograph images are transformed into feature images with the histogram near bimodal distribution. Finally, feature images are segmented into normal and bone-loss regionsby Otsu's auto-thresholding. We test on eight periodontitis radiograph images using the proposed ABL-Imm, the methods with only the feature of mm-H or the intensity, and a method based on level set segmentation, respectively. Experimental results showed that among all the test methods, our proposed ABL-Imm has the highest average TPVF and the lowest average FPVF, when compared with the ground truth (GT) provided by dentists.

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Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model

Cited by 56 publications

References 26 publications

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

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