• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.7-12
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• 2009

In this paper, we propose a directional feature extraction method for off-line handwritten numerals by using the morphological operations. Direction features are obtained from four directional line images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral lines. Conventional method for extracting directional features uses Kirsch masks which generate edge-shaped double line images for each direction, whereas our method uses directional erosion operations and generate single line images for each direction. To apply these directional erosion operations to the numeral image, preprocessing steps such as thinning and dilation are required, but resultant directional lines are more similar to numeral lines themselves. Our four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. For obtaining the higher recognition rates of the handwrittern numerals, we use the multiple feature which is comprised of our proposed feature and the conventional features of a kirsch directional feature and a concavity feature. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the CENPARMI numeral database of Concordia University, we have achieved a recognition rate of 98.35%.