Among the detected small nodules sized from 3 to 30mm in CT images, a significant portion is undetermined in terms of malignancy which needs biopsy or other follow-up means, resulting in excessive risk and cost. Therefore, predicting the malignancy of the nodules becomes a clinically desirable task. Based on the previous study of texture features extracted from gray-tone spatial-dependence matrices, this study aims to find more efficient texture features or image texture markers in discriminating the nodule malignancy. Two new image texture markers (median and variance) are proposed to classify the small nodules into different malignant levels, thus the risk prediction could be performed through image analysis. These two new image texture markers can minimize the effect of outliers in the feature series, thus can reduce the noise influence to the feature classification. Total 1,353 nodule samples selected from the Lung Image Database Consortium were used to evaluate the efficiency of the proposed new features. All the classification results are shown in the ROC curves and tabulated by the AUC values. The classification outcomes from (1) the most likely and likely benign nodules vs. the most likely and likely malignant nodules, (2) the most likely vs. likely benign nodules, and (3) the most likely vs. likely malignant nodules, are 0.9125±0.0096, 0.9239±0.0147, and 0.8888±0.0197, respectively, in terms of the largest AUC values. From the experimental outcomes on different malignant levels, the two new image texture markers from nodule volumetric CT image data have shown encouraging performance for the risk prediction.
Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.
Texture feature from chest CT images for malignancy assessment of pulmonary nodules has become an un-ignored and efficient factor in Computer-Aided Diagnosis (CADx). In this paper, we focus on extracting as fewer as needed efficient texture features, which can be combined with other classical features (e.g. size, shape, growing rate, etc.) for assisting lung nodule diagnosis. Based on a typical calculation algorithm of texture features, namely Haralick features achieved from the gray-tone spatial-dependence matrices, we calculated two dimensional (2D) and three dimensional (3D) Haralick features from the CT images of 905 nodules. All of the CT images were downloaded from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), which is the largest public chest database. 3D Haralick feature model of thirteen directions contains more information from the relationships on the neighbor voxels of different slices than 2D features from only four directions. After comparing the efficiencies of 2D and 3D Haralick features applied on the diagnosis of nodules, principal component analysis (PCA) algorithm was used to extract as fewer as needed efficient texture features. To achieve an objective assessment of the texture features, the support vector machine classifier was trained and tested repeatedly for one hundred times. And the statistical results of the classification experiments were described by an average receiver operating characteristic (ROC) curve. The mean value (0.8776) of the area under the ROC curves in our experiments can show that the two extracted 3D Haralick projected features have the potential to assist the classification of benign and malignant nodules.
To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of
lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this
proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray
level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To
evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.
The task of effectively segmenting colon areas in CT images is an important area of interest in medical imaging field.
The ability to distinguish the colon wall in an image from the background is a critical step in several approaches for
achieving larger goals in automated computer-aided diagnosis (CAD). The related task of polyp detection, the ability to
determine which objects or classes of polyps are present in a scene, also relies on colon wall segmentation. When
modeling each tissue type as a conditionally independent Gaussian distribution, the tissue mixture fractions in each voxel via the modeled unobservable random processes of the underlying tissue types can be estimated by maximum a
posteriori expectation-maximization (MAP-EM) algorithm in an iterative manner. This paper presents, based on the
assumption that the partial volume effect (PVE) could be fully described by a tissue mixture model, a theoretical solution to the MAP-EM segmentation algorithm. However, the MAP-EM algorithm may miss some small regions which also belong to the colon wall. Combining with the shape constrained model, we present an improved algorithm which is able to merge similar regions and reserve fine structures. Experiment results show that the new approach can refine the jagged-like boundaries and achieve better results than merely exploited our previously presented MAP-EM algorithm.