In screening for primary lung cancer with plain chest radiography, computer-aided diagnosis systems are being developed to reduce chest radiologists' task and the risk of missing positive cases. We evaluated a difference filter that enhances nodule densities in the preprocessing of chest X-ray images. Since ribs often affect detection of pulmonary nodules, we designed an eye-shaped filter to fit the rib shape. Although this filter increased the nodule detection rate, it could not detect nodules near the thoracic wall. The thoracic wall was then outlined by computers with Hough transformation for line detection. On the basis of the outline, the direction of the eye-shaped filter was determined. With this technique, the filter was not affected by considerable changes in the shape of anatomical structures, such as ribs and the thoracic wall, and could detect pulmonary nodules regardless of their location.

This paper proposes a new approach to deal with the various quality of the reference impressions by measuring the seal to register as 3D (three-dimensional) image, that is, range image. By registering a seal as 3D image, it becomes possible to construct various 2D impressions from it according to the affixing conditions of the reference impression such as the affixing slant, the affixing pressure, the state of the ink on the seal surface and so on. Then, the accurate and easy identification of the seals will be possible by comparing the constructed impression with the reference impression. The performance is verified by experiment, and the result shows that plural 2D impressions according to the affixing conditions can be constructed from only one 3D image of the registered seal.

A method for the recognition of Arabic printed scripts entered from an image scanner is presented. The method uses the Hough transformation (HT) to extract features, Dynamic programming (DP) matching technique, and a topological classifier to recognize the characters. A process of characters recognition is further divided into four parts: preprocessing, segmentation of a word into characters, features extraction, and characters identification. The preprocessing consists of the following steps: smoothing to remove noise, baseline drift correction by using HT, and lines separation by making an horizontal projection profile. After preprocessing, Arabic printed words are segmented into characters by analysing the vertical and the horizontal projection profiles using a threshold. The character or stroke obtained from the segmentation process is normalized in size, then thinned to provide it skeleton from which features are extracted. As in the procedure of straight lines detection, a threshold is applied to every cell and those cells whose count is greater than the threshold are selected. The coordinates (R, θ) of the selected cells are the extracted features. Next, characters are classified in two steps: In the first one, the character main body is classified using DP matching technique, and features selected in the HT space. In the second one, simple topological features extracted from the geometry of the stress marks are used by the topological classifier to completely recognize the characters. The topological features used to classify each type of the stress mark are the width, the height, and the number of black pixels of the stress marks. Knowing both the main group of the character body and the type of the stress mark (if any), the character is completely identified.