Heart sound is an acoustic wave generated by the mechanical movement of the heart and blood flow, and is a complicated, non-stationary signal composed of many signal sources. It can be divided into normal heart sounds and heart murmurs. Murmurs are abnormal signals that appear over wider ranges of frequency than normal heart sounds. They are generated at random spots in the whole period of heart sounds. The recognition of heart sounds is to differentiate heart murmurs through patterns that appear according to the generation time of murmurs. In this paper, a group of heart sounds was classified into normal heart sounds, pre-systolic murmurs, early systolic murmurs, late systolic murmurs, early diastolic murmurs, and continuous murmurs. The suggested algorithm was standardized by re-sampling and then added as an input to the neural network through wavelet transform. The neural network used Error Back - Propagation algorithm, which is a representative learning method, and controlled the number of hidden layers and the learning rate for optimal construction of networks. As a result of recognition, the suggested algorithm obtained a higher recognition rate than that of existing research methods. The best result was obtained with the average of 88% of the recognition rate when it consisted of 15 hidden layers. The suggested algorithm was considered effective for the recognition of automatic diagnosis of heart sound recognition programs.

In this paper, near real time digital radiography system was implemented for the automatic verification of local errors between simulation plan and radiation therapy. Portal image could be acquired through video camera, image board and PC after therapy radiation was converted into light by a metal/fluorescent screen. Considering the divergence according to the distance between the source and the plate, we made a 340 340 12 cm3 basis point plate on which five rods of 4 cm height and 8 mm diameter lead (Pb) were built to display reference points on the simulator and the portal image. We converted the portal image into the binary image using the optimal threshold value which was gotten through the histogram analysis of the acquired portal image using the basis point plate. we got the location information of the iso-center and basis points from the binary image, and removed the systematic errors which were from the differences between the simulation plan and the portal image. Field size which was measured automatically by optimal threshold portal image, was verified with simulation plan. Anatomic errors were automatically detected and verified with the normalized simulation and the portal image by pattern matching method after irradiating a part of the radiation. Therapy efficiency was improved and radiation side effects were reduced by these techniques, so exact radiation treatment are expected.

This paper describes the improvement of the prior personal computer based karyotyping system of Giemsastained chromosomes that had resolution of 256256 pixels. To upgrade the accuracy of karyotyping the resolution of the image acquisition board was increased to 512512 pixels and the algorithm of finding the centromere of a chromosome was improved. The results of the karyotyping using this improved system with an image of chromosomes taken from the Rana Amurensis had been better than the previous one.