Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules.
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-dimen...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 1; pp. 99 - 116 |
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| Autores principales: | , , , , , , , , , |
| Formato: | algorithm diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103745374&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103745374 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2015 vid: 28 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103745374 100631423 10.1007/s10278-014-9718-8 NLM25117512 PMC4305062 103745374 ppf: 99 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules. aug: au: Han, Fangfang Wang, Huafeng Zhang, Guopeng Han, Hao Song, Bowen Li, Lihong Moore, William Lu, Hongbing Zhao, Hong Liang, Zhengrong affil: Department of Radiology, State University of New York, Stony Brook 11794 USA sug: subj: Diagnosis, Computer Assisted Solitary Pulmonary Nodule Diagnosis Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Solitary Pulmonary Nodule Classification Comparative Studies Evaluation Research ROC Curve Descriptive Statistics P-Value Human Funding Source ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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