Markov random field (MRF) model has been widely used in Bayesian image reconstruction to reconstruct piecewise smooth images in the presence of noise, such as in low-dose X-ray computed tomography (LdCT). While it can preserve edge sharpness via edge-preserving potential function, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it compromises clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodule or colon polyp. This study aims to shift the edge preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF’s neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of lung, bone, fat, muscle, etc. from previous full-dose CT scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of proposed reconstruction framework, experiments using clinical patient scans (with lung nodule or colon polyp) were conducted. The experimental outcomes showed noticeable gain by the a priori knowledge for LdCT image reconstruction with the well-known Haralick texture measures. Thus, it is conjectured that texture-preserving LdCT reconstruction has advantages over edge-preserving regional smoothing paradigm for texture-specific clinical applications.
Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.
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.
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