Feature classification plays an important role in computer-aided diagnosis (CADx) of suspicious lesions or polyps in this concerned study. As one of the simplest machine learning algorithms, the k-nearest neighbor (k-NN) classifier has been widely used in many classification problems. However, the k-NN classifier has a drawback that the majority classes will dominate the prediction of a new sample. To mitigate this drawback, efforts have been devoted to set weight on each neighbor to avoid the influence of the “majority” classes. As a result, various weighted or wk-NN strategies have been explored. In this paper, we explored an alternative strategy, called “distance weighted inside disc” (DWID) classifier, which is different from the k-NN and wk-NN by such a way that it classifies the test point by assigning a corresponding label (instead a weight) with consideration of only those points inside the disc whose center is the test point instead of the k-nearest points. We evaluated this new DWID classifier with comparison to the k-NN, wk-NN, support vector machine (SVM) and random forest (RF) classifiers by experiments on a database of 153 polyps, including 116 neoplastic (malignance) polyps and 37 hyperplastic (benign) polyps, in terms of CADx or differentiation of benign from malignancy. The evaluation outcomes were documented quantitatively by the Receiver Operating Characteristics (ROC) analysis and the merit of area under the ROC curve (AUC), which is a well-established evaluation criterion to various classifiers. The results showed noticeable gain on the polyp differentiation by this new classifier according to the AUC values, as compared to the k-NN and wk-NN, as well as the SVM and RF. In the meantime, this new classifier also showed a noticeable reduction of computing time.
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.
Accurate assessment of colorectal polyp size is of great significance for early diagnosis and management of colorectal cancers. Due to the complexity of colon structure, polyps with diverse geometric characteristics grow from different landform surfaces. In this paper, we present a new colon decomposition approach for polyp measurement. We first apply an efficient maximum a posteriori expectation-maximization (MAP-EM) partial volume segmentation algorithm to achieve an effective electronic cleansing on colon. The global colon structure is then decomposed into different kinds of morphological shapes, e.g. haustral folds or haustral wall. Meanwhile, the polyp location is identified by an automatic computer aided detection algorithm. By integrating the colon structure decomposition with the computer aided detection system, a patch volume of colon polyps is extracted. Thus, polyp size assessment can be achieved by finding abnormal protrusion on a relative uniform morphological surface from the decomposed colon landform. We evaluated our method via physical phantom and clinical datasets. Experiment results demonstrate the feasibility of our method in consistently quantifying the size of polyp volume and, therefore, facilitating characterizing for clinical management.
Colorectal cancer is the third most common type of cancer. However, this disease can be prevented by detection and removal of precursor adenomatous polyps after the diagnosis given by experts on computer tomographic colonography (CTC). During CTC diagnosis, the radiologist looks for colon polyps and measures not only the size but also the malignancy. It is a common sense that to segment polyp volumes from their complicated growing environment is of much significance for accomplishing the CTC based early diagnosis task. Previously, the polyp volumes are mainly given from the manually or semi-automatically drawing by the radiologists. As a result, some deviations cannot be avoided since the polyps are usually small (6~9mm) and the radiologists’ experience and knowledge are varying from one to another. In order to achieve automatic polyp segmentation carried out by the machine, we proposed a new method based on the colon decomposition strategy. We evaluated our algorithm on both phantom and patient data. Experimental results demonstrate our approach is capable of segment the small polyps from their complicated growing background.
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.
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.
Human colon has complex structures mostly because of the haustral folds. Haustral folds are thin flat protrusions on the
colon wall, which inherently attached on the colon wall. These structures may complicate the shape analysis for
computer-aided detection of colonic polyps (CADpolyp); however, they can serve as solid reference during image
interpretation in computed tomographic colonography (CTC). Therefore, in this study, based on a clear model of the
haustral fold boundaries, we employ level set method to automatically segment the fold surfaces. We believe the
segmented folds have the potential to significantly benefit various post-procedures in CTC, e.g., supine-prone
registration, synchronized image interpretation, automatic polyp matching, CADpolyp, teniae coli extraction, etc. For
the first time, with assistance from physician experts, we established the ground truth of haustral fold boundaries of 15
real patient data from two medical centers, based on which we evaluated our algorithm. The results demonstrated that
about 92.7% of the folds are successfully detected. Furthermore, we explored the segmented area ratio (SAR), i.e., the
ratio between the areas of the intersection and the union of the expert-drawn and the automatically-segmented folds, to
measure the accuracy of the segmentation algorithm. The averaged result of SAR=86.2% shows a good match between
the ground truth and our segmentation results.
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