Ultrasound imaging can provide radiation-free, non-invasive, low cost, and convenient for disease detection. However, speckle effect makes it noisy and thus reduces its overall diagnostic abilities in disease analysis. This paper develops a real time system to analyze chronic kidney disease (CKD) using only Ultrasound images. As we know, this is the first work to analyze CKD stages of patients directly from ultrasound images without using any blood examination such as Creatinine index. To build the scoring index, this paper uses Nakagami distribution and Local Binary Pattern (LBP) to model the scattering properties of CKD patients' ultrasound images. In addition, we find the age distribution is also important for CKD stage analysis. After integration, a codebook concept is adopted to extract important visual codes to describe various texture and scattering characteristics of each CKD stage. Then, an ensemble scheme is proposed for CKD stage prediction and classification by separating input ultrasound images to several grids and then integrating different classifiers trained on these grids to build a strong CKD stage classifier via SVM. Experimental results demonstrate the sensitivity and specificity of this system up to 93.82% and 83.34%, respectively.
This paper proposes a new vehicle color classification scheme to identify vehicles with their colors. To detect vehicles from roads, the paper proposes a novel symmetrical descriptor to determine the ROI of each vehicle without using any motion features. This scheme provides two advantages; there is no need of background subtraction and it is extremely efficient for real-time applications. After detection, a novel color-correction technique is proposed to reduce the color changes of vehicles so that vehicles can be more accurately identified. The major challenge in vehicle color identification is there are many shade (or confused) colors among vehicles. This paper proposes a new concept that the vehicles with different chromatic attributes should be separately trained even though they are in the same color category. With this concept, a novel tree-based classifier can be constructed to classify vehicles at different stages according to their chromatic strengths. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors.
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