title: Texture feature analysis for computer-aided diagnosis on pulmonary nodules.
abstract: 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.
authors.affiliation: Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA.
authors.forename: Fangfang
authors.initials: F
authors.lastname: Han
authors.forename: Huafeng
authors.initials: H
authors.lastname: Wang
authors.forename: Guopeng
authors.initials: G
authors.lastname: Zhang
authors.forename: Hao
authors.initials: H
authors.lastname: Han
authors.forename: Bowen
authors.initials: B
authors.lastname: Song
authors.forename: Lihong
authors.initials: L
authors.lastname: Li
authors.forename: William
authors.initials: W
authors.lastname: Moore
authors.forename: Hongbing
authors.initials: H
authors.lastname: Lu
authors.forename: Hong
authors.initials: H
authors.lastname: Zhao
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
mesh_terms: D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*