Youngjoo Lee

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The motivation is to introduce new shape features and optimize the classifier to improve performance of differentiating obstructive lung diseases, based on high-resolution computerized tomography (HRCT) images. Two hundred sixty-five HRCT images from 82 subjects were selected. On each image, two experienced radiologists selected regions of interest (ROIs)(More)
Machine classifiers have been used to automate quantitative analysis and avoid intra-inter-reader variability in previous studies. The selection of an appropriate classification scheme is important for improving performance based on the characteristics of the data set. This paper investigated the performance of several machine classifiers for(More)
To improve time and accuracy in differentiating diffuse interstitial lung disease for computer-aided quantification, we introduce a hierarchical support vector machine which selects a class by training a binary classifier at each node in a hierarchy, thus allowing each classifier to use a class-specific quasi-optimal feature set. In addition, the(More)
We propose the use of a context-sensitive support vector machine (csSVM) to enhance the performance of a conventional support vector machine (SVM) for identifying diffuse interstitial lung disease (DILD) in high-resolution computerized tomography (HRCT) images. Nine hundred rectangular regions of interest (ROIs), each 20 × 20 pixels in size and consisting(More)
KAIST ABSTRACT 1 This paper presents a new optimization method to reduce the hardware complexity of syndrome calculation in strong BCH decoding. All the operations required in the parallel syndrome calculation are reformulated as a single matrix computation to enlarge the search area for common sub-expressions. The computational complexity of syndrome(More)
Keywords: Multiclass support vector machine Binary tree architecture Genetic algorithm Partially mapped crossover a b s t r a c t Support vector machine (SVM) with a binary tree architecture is popular since it requires the minimum number of binary SVM to be trained and tested. Many efforts have been made to design the optimal binary tree architecture.(More)