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Deformed microstructure of the adiabatic shear bands in zirconium alloy impacted by split Hopkinson pressure bar at a strain rate of about 2300 s-1 was characterized systemically. Four different strains of zirconium alloy subjected to impact loading at same strain rate were designed by means of strain stopper rings. At a strain of 0.30, the transformed bands were distinguished at the tip of crack. The transformed bands mainly composed of the ultrafine and equiaxed grains with the mean diameter of about 100~300 nm were found by a dual beam FIB/TEM system. Besides ultrafine grains in the transformed bands, nanometer fine grain was observed and confirmed by HRTEM. Thus, the transformed bands mainly composed of the mixed microstructure including nanometer and ultrafine grains were confirmed, and no phase transformation and amorphization in transformed bands take place. [ABSTRACT FROM AUTHOR]

This paper proposes a new two-scan algorithm for labeling connected components in binary images. In the first scan of our algorithm, all conventional two-scan labeling algorithms process image lines one by one and process pixels one by one. In comparison, we process image lines two by two and process image pixels two by two. By our algorithm, the average times for checking the neighbor pixels for processing a foreground pixel will decrease, which leads to an efficient labeling processing. Experimental results on various types of images demonstrated that our method is more efficient than conventional label-equivalence-based labeling algorithms. [ABSTRACT FROM AUTHOR]

Lung cancer has been the most common cancer in the world. Early detection is the most important for reducing the death due to lung cancer. Chest radiography has been widely and frequently used for detection and diagnosis on lung cancer. To assess pathological changes in chest radiographs, radiologists often compare the previous chest radiograph and the current one obtained from the same patient at different times. A temporal subtraction image, which is constructed subtracting the previous radiograph from the current one, is often used to support this comparison work. This paper presents a mutual-information-based image registration method for chest-radiograph temporal subtraction. First, we extract the lung regions of original radiographs. Then we get the center lines of lungs by the outer contours of lungs. We set the centre lines of lungs verticality and coincident in horizontal. We extract rib cages from the images of lung regions after centre line aligning. Then we constantly transform the rib cage image of the previous radiograph. As transforming, we calculate the mutual information between two rib cages images. We improve the transform parameters based on the mutual information, and subtract the radiographs translated by using the best parameters to construct the temporal subtraction image. [ABSTRACT FROM PUBLISHER]

Lung cancer has been the most common cancer in the world. Early detection is the most important for reducing the death due to lung cancer. Chest radiography has been widely and frequently used for detection and diagnosis on lung cancer. To assess pathological changes in chest radiographs, radiologists often compare the previous chest radiograph and the current one obtained from the same patient at different times. A temporal subtraction image, which is constructed subtracting the previous radiograph from the current one, is often used to support this comparison work. This paper presents a multilayered partitioning image registration method for chest-radiograph temporal subtraction. First, we used global matching based on the mutual information of ribs. Then, we divide the images after global matching into four groups of temple subareas used multilayered partitioning method. For individual local subarea in each group, we use a genetic algorithm to efficiently find its corresponding area in the previous image. By the result of genetic algorithm, we construct the new matching image according to the four groups of temple subareas, and subtract the matching image to construct the temporal subtraction image. [ABSTRACT FROM PUBLISHER]

This paper reviews the application of artificial neural networks in medical image preprocessing, in medical image object detection and recognition. Main advantages and drawbacks of artificial neural networks were discussed. By this survey, the paper try to answer what the major strengths and weakness of applying neural networks for medical image processing would be. [ABSTRACT FROM AUTHOR]