• Journal of Korean Neurosurgical Society
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• v.29 no.1
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• pp.58-65
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• 2000

Objective : Thoracolumbar junction is second most common level of injury next to cervical spine. The object of this study is to study the usefulness of surgical titanium mesh instead of bone graft, as well as to evaluate the correction of spinal deformity and safety of early ambulation in patients with injury at thoracolumbar junction. Patients and Methods : This review included 51 patients who were operated from July 1994 to December 1997. The injured spine is considered to be unstable, if it shows involvement of two or more columns, translatory displacement more than 3.5mm, decrease more than 35% in height of vertebral body and progression of malalignment in serial X-ray. The decision to operate was determined by (1) compression of spinal cord or cauda eguina, (2) unstable fracture, (3) malalignment and (4) fracture dislocation. The procedure consisted of anterior decompression through corpectomy and internal fixation with anterior instrument and surgical titanium mesh which was impacted with gathered bone chip from corpectomy. Results : Fifty-one patients were followed up for at least 12 months. The main causes of injury were fall and vehicle accident. The twelfth thoracic and the first and the second lumbar vertebrae were frequently involved. Complete neural decompression was possible under direct vision in all cases. Kyphotic angulation occurred in a patient. Radiologic evaluation showed correction of deformity and no distortion or loosening of surgical titanium mesh with satisfactory fixation postoperatively. Conclusions : We could obtain neurological improvement, relief of pain, immediate stabilization and early return to normal activities postoperatively. Based on these results, authors recommend anterior decompression and internal fixation with surgical titanium mesh in thoracolumbar unstable spine injuries.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.