A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

Cirujeda, Pol; Cid, Yashin Dicente; Müller, Henning; Rubin, Daniel L.; Aguilera, Todd A.; Loo, Billy W.; Diehn, Maximilian; Binefa, Xavier; Depeursinge, Adrien

doi:10.1109/tmi.2016.2591921

Cited by 36 publications

(18 citation statements)

References 49 publications

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“…New ''engineered'' or ''handcrafted'' features with potentially higher discriminative power or better properties are continuously being developed. CoLIAGe (Cooccurrence of Local Anisotropic Gradient Orientations) (68), a metabolic gradient (69), or 3-dimensional Riesz-covariance textures (70) are examples of such new features with a potentially higher differentiation power compared with standard textural features. A novel metric for quantifying PET heterogeneity was also proposed as a more intuitive and simple alternative to textural features; this method involves summing voxelwise distributions of differential SUVs, weighted by the distance of SUV differences among neighboring voxels from the center of the tumor (71).…”

Section: Feature Calculationmentioning

confidence: 99%

Radiomics: Data Are Also Images

et al. 2019

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The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.

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Section: Feature Calculationmentioning

confidence: 99%

Radiomics: Data Are Also Images

et al. 2019

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“…However, many nodule heterogeneity patterns cannot be visualized. On the other hand, various computer-aided image features have been proposed to quantify such heterogeneity, in addition to those features in alignment with the radiologist's description, such as size, shape, margin, attenuation, and growth rate (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Although extensive investigations have revealed that different computer-aided image features are associated with lung cancer development, computer-aided diagnosis (CAD) approaches have not been translated into clinical practice because it is unclear whether such approaches can provide additive information beyond the radiographic characteristics used by radiologists in routine clinical practice.…”

Section: Technical Parametersmentioning

confidence: 99%

Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study

Huang¹,

et al. 2018

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Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists. Materials and Methods A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4-20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set to predict biopsy outcomes. Receiver operating characteristic analysis was used to compare the prediction accuracy of CAD with the NLST investigator's diagnosis and readings from three experienced and board-certified thoracic radiologists who used contemporary clinical practice guidelines. Results In the validation cohort, the area under the receiver operating characteristic curve for CAD was 0.9154. By default, the sensitivity, specificity, and PPV of the NLST investigators were 1.00, 0.00, and 0.43, respectively. The sensitivity, specificity, PPV, and negative predictive value of CAD and the three radiologists' combined reading were 0.95, 0.88, 0.86, and 0.96 and 0.70, 0.69, 0.64, and 0.75, respectively. Conclusion CAD could increase PPV and reduce the false-positive rate in the early diagnosis of lung cancer. RSNA, 2017 Online supplemental material is available for this article.

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“…Material image classification from texture contents is to assign one or more category labels to an image. It is one of the most fundamental problems in a wide range of applications such as industrial inspection [1], image retrieval [2], medical imaging [3,4], remote sensing [5,6], object recognition, and facial recognition [7][8][9]. In the general framework of image classification, feature coding techniques for bag-of-features methodologies have proven their efficiency in the recent literature.…”

Section: Introductionmentioning

confidence: 99%

Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

Ilea

Bombrun

Said

et al. 2017

Lecture Notes in Computer Science

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This paper introduces a novel local model for the classification of covariance matrices: the co-occurrence matrix of covariance matrices. Contrary to state-of-the-art models (BoRW, R-VLAD and RFV), this local model exploits the spatial distribution of the patches. Starting from the generative mixture model of Riemannian Gaussian distributions, we introduce this local model. An experiment on texture image classification is then conducted on the VisTex and Outex_TC000_13 databases to evaluate its potential.

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A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

Cited by 36 publications

References 49 publications

Radiomics: Data Are Also Images

Radiomics: Data Are Also Images

Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study

Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

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