Ischemic stroke has great correlation with carotid atherosclerosis and is mostly caused by vulnerable plaques. It’s
particularly important to analysis the components of plaques for the detection of vulnerable plaques. Recently plaque
analysis based on multi-contrast magnetic resonance imaging has attracted great attention. Though multi-contrast MR
imaging has potentials in enhanced demonstration of carotid wall, its performance is hampered by the misalignment of
different imaging sequences. In this study, a coarse-to-fine registration strategy based on cross-sectional images and wall
boundaries is proposed to solve the problem. It includes two steps: a rigid step using the iterative closest points to register
the centerlines of carotid artery extracted from multi-contrast MR images, and a non-rigid step using the thin plate spline
to register the lumen boundaries of carotid artery. In the rigid step, the centerline was extracted by tracking the crosssectional
images along the vessel direction calculated by Hessian matrix. In the non-rigid step, a shape context descriptor
is introduced to find corresponding points of two similar boundaries. In addition, the deterministic annealing technique is
used to find a globally optimized solution. The proposed strategy was evaluated by newly developed three-dimensional,
fast and high resolution multi-contrast black blood MR imaging. Quantitative validation indicated that after registration,
the overlap of two boundaries from different sequences is 95%, and their mean surface distance is 0.12 mm. In conclusion,
the proposed algorithm has improved the accuracy of registration effectively for further component analysis of carotid
plaques.
Differentiating bladder tumors from wall tissues is of critical importance for the detection of invasion depth and cancer staging. The textural features embedded in bladder images have demonstrated their potentials in carcinomas detection and classification. The purpose of this study was to investigate the feasibility of differentiating bladder carcinoma from bladder wall using three-dimensional (3D) textural features extracted from MR bladder images. The widely used 2D Tamura features were firstly wholly extended to 3D, and then different types of 3D textural features including 3D features derived from gray level co-occurrence matrices (GLCM) and grey level-gradient co-occurrence matrix (GLGCM), as well as 3D Tamura features, were extracted from 23 volumes of interest (VOIs) of bladder tumors and 23 VOIs of patients’ bladder wall. Statistical results show that 30 out of 47 features are significantly different between cancer tissues and wall tissues. Using these features with significant differences between these two types of tissues, classification performance with a supported vector machine (SVM) classifier demonstrates that the combination of three types of selected 3D features outperform that of using only one type of features. All the observations demonstrate that significant textural differences exist between carcinomatous tissues and bladder wall, and 3D textural analysis may be an effective way for noninvasive staging of bladder cancer.
The ever-growing death rate and the high recurrence of bladder cancer make the early detection and appropriate followup procedure of bladder cancer attract more attention. Compare to optical cystoscopy, image-based studies have revealed its potentials in non-invasive observations of the abnormities of bladder recently, in which MR imaging turns out to be a better choice for bladder evaluation due to its non-ionizing and high contrast between urine and wall tissue. Recent studies indicate that bladder wall thickness tends to be a good indicator for detecting bladder wall abnormalities. However, it is difficult to quantitatively compare wall thickness of the same subject at different filling stages or among different subjects. In order to explore thickness variations at different bladder filling stages, in this study, we preliminarily investigate the relationship between bladder wall thickness and bladder volume based on a MRI database composed of 40 datasets acquired from 10 subjects at different filling stages, using a pipeline for thickness measurement and analysis proposed in our previous work. The Student’s t-test indicated that there was no significant different on wall thickness between the male group and the female group. The Pearson correlation analysis result indicated that negative correlation with a correlation coefficient of -0.8517 existed between the wall thickness and bladder volume, and the correlation was significant(p <0.01). The corresponding linear regression equation was then estimated by the unary linear regression. Compared to the absolute value of wall thickness, the z-score of wall thickness would be more appropriate to reflect the thickness variations. For possible abnormality detection of a bladder based on wall thickness, the intra-subject and inter-subject thickness variation should be considered.
KEYWORDS: Feature extraction, Feature selection, CAD systems, Computer aided diagnosis and therapy, Colon, Bladder cancer, Signal to noise ratio, Detection and tracking algorithms, Classification systems
Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier’s suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.
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.
Various types of features, e.g., geometric features, texture features, projection features etc., have been introduced for
polyp detection and differentiation tasks via computer aided detection and diagnosis (CAD) for computed tomography
colonography (CTC). Although these features together cover more information of the data, some of them are statistically highly-related to others, which made the feature set redundant and burdened the computation task of CAD. In this paper, we proposed a new dimension reduction method which combines hierarchical clustering and principal component analysis (PCA) for false positives (FPs) reduction task. First, we group all the features based on their similarity using hierarchical clustering, and then PCA is employed within each group. Different numbers of principal components are selected from each group to form the final feature set. Support vector machine is used to perform the classification. The results show that when three principal components were chosen from each group we can achieve an area under the curve of receiver operating characteristics of 0.905, which is as high as the original dataset. Meanwhile, the computation time is reduced by 70% and the feature set size is reduce by 77%. It can be concluded that the proposed method captures the most important information of the feature set and the classification accuracy is not affected after the dimension reduction. The result is promising and further investigation, such as automatically threshold setting, are worthwhile and are under progress.
Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect
distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems.
Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and
region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a
retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and
texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our
previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp
detection. Preliminary experiments indicated that the integration of morphological features with texture features could
improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with
the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the
average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.
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