Early detection and treatment of lung cancer is one of the most effective means to reduce cancer mortality; chest
X-ray radiography has been widely used as a screening examination or health checkup. The new examination
method and the development of computer analysis system allow obtaining respiratory kinetics by the use of flat
panel detector (FPD), which is the expanded method of chest X-ray radiography. Through such changes functional
evaluation of respiratory kinetics in chest has become available. Its introduction into clinical practice is expected in
the future. In this study, we developed the computer analysis algorithm for the purpose of detecting lung nodules
and evaluating quantitative kinetics. Breathing chest radiograph obtained by modified FPD was converted into 4
static images drawing the feature, by sequential temporal subtraction processing, morphologic enhancement
processing, kinetic visualization processing, and lung region detection processing, after the breath synchronization
process utilizing the diaphragmatic analysis of the vector movement. The artificial neural network used to analyze
the density patterns detected the true nodules by analyzing these static images, and drew their kinetic tracks. For the
algorithm performance and the evaluation of clinical effectiveness with 7 normal patients and simulated nodules,
both showed sufficient detecting capability and kinetic imaging function without statistically significant difference.
Our technique can quantitatively evaluate the kinetic range of nodules, and is effective in detecting a nodule on a
breathing chest radiograph. Moreover, the application of this technique is expected to extend computer-aided
diagnosis systems and facilitate the development of an automatic planning system for radiation therapy.
In the picture archiving and communication system (PACS) environment, it is important that all images be stored in
the correct location. However, if information such as the patient's name or identification number has been entered
incorrectly, it is difficult to notice the error. The present study was performed to develop a system of patient collation
automatically for dynamic radiogram examination by a kinetic analysis, and to evaluate the performance of the system.
Dynamic chest radiographs during respiration were obtained by using a modified flat panel detector system. Our
computer algorithm developed in this study was consisted of two main procedures, kinetic map imaging processing, and
collation processing. Kinetic map processing is a new algorithm to visualize a movement for dynamic radiography;
direction classification of optical flows and intensity-density transformation technique was performed. Collation
processing consisted of analysis with an artificial neural network (ANN) and discrimination for Mahalanobis'
generalized distance, those procedures were performed to evaluate a similarity of combination for the same person.
Finally, we investigated the performance of our system using eight healthy volunteers' radiographs. The performance
was shown as a sensitivity and specificity. The sensitivity and specificity for our system were shown 100% and 100%,
respectively. This result indicated that our system has excellent performance for recognition of a patient. Our system
will be useful in PACS management for dynamic chest radiography.
The purpose of this study was to develop of kinetic analysis method for PACS management and computer-aided diagnosis. We obtained dynamic chest radiographs (512x512, 8bit, 4fps, and 1344x1344, 12bit, 3fps) of five healthy volunteers during respiration using an I.I. system twice, and one healthy volunteer using dynamic FPD system. Optical flows of images were obtained using customized block matching technique, and were divided into a direction, and transformed into the RGB color. Density was determined by the sum pixel length of movement during respiration phase. The made new static image was defined as the "kinetic map". The evaluation of patient's collation was performed with a template matching to the three colors. The same person's each correlation value and similar-coefficient which is defined in this study were statistically significant high (P<0.01). We used the artificial neural network (ANN) for the judgment of the same person. Five volunteers were divided into two groups, three volunteers and two volunteers became a training signal and unknown signal. Correlation value and similar-coefficient was used for the input signal, and ANN was designed so that the same person's probability might be outputted. The average of the specificity of the unknown signal obtained 98.2%. The kinetic map including the imitation tumor was used for the simulation. The tumor was detected by temporal subtraction of kinetic map, and then the superior sensitivity was obtained. Our analysis method was useful in risk management and computer-aided diagnosis.
When network distribution of movie files was considered as reference, it could be useful that the lossy compression movie files which has small file size. We chouse three kinds of coronary stricture movies with different moving speed as an examination object; heart rate of slow, normal and fast movies. The movies of MPEG-1, DivX5.11, WMV9 (Windows Media Video 9), and WMV9-VCM (Windows Media Video 9-Video Compression Manager) were made from three kinds of AVI format movies with different moving speeds. Five kinds of movies that are four kinds of compression movies and non-compression AVI instead of the DICOM format were evaluated by Thurstone's method. The Evaluation factors of movies were determined as "sharpness, granularity, contrast, and comprehensive evaluation." In the virtual bradycardia movie, AVI was the best evaluation at all evaluation factors except the granularity. In the virtual normal movie, an excellent compression technique is different in all evaluation factors. In the virtual tachycardia movie, MPEG-1 was the best evaluation at all evaluation factors expects the contrast. There is a good compression form depending on the speed of movies because of the difference of compression algorithm. It is thought that it is an influence by the difference of the compression between frames. The compression algorithm for movie has the compression between the frames and the intra-frame compression. As the compression algorithm give the different influence to image by each compression method, it is necessary to examine the relation of the compression algorithm and our results.
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