• Journal of the Korean Society of Radiology
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• v.8 no.7
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• pp.371-376
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• 2014

The process of flip-chip bump bonding, Au wire bonding and encapsulation were sucessfully developed and modularized. The CdTe sensor and ROIC were optimally jointed together at $150^{\circ}C$ and $270^{\circ}C$ respectively under24.5 N for 30s. To make SnAg bump on ROIC easy to be bonded, the higher bonding temperature was established than CdTe sensor's. In addition, the bonding pressure was lowered minimally because CdTe Sensor is easier to break than Si Sensor. CdTe multi-energy sensor module observed were no electrical failures in the joints using developed flip chip bump bonding and Au wire bonding process. As a result of measurement, shearing force was $2.45kgf/mm^2$ and, it is enough bonding force against threshold force, $2kgf/mm^2s$.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1233-1241
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• 2009

Over the 50 percents of women who are older than 45 years have osteoporosis. Because people hardly recognize this disease by themselves, the researches that measure bone mineral density have been doing widely to detect osteoporosis in the early stage. The most widely used methods for bone mineral density measurement are based on the X-ray imaging. Among them, DEXA(Dual-energy X-ray Absorptiometry) imaging is one of the important methods in bone mineral density measurement. DEXA images are useful methods to increase diagnosis efficiency by reducing anatomic noise as two images obtained from two different energy levels. However, it has some problems to a calibration parameter determined by the heuristic method for bone extraction. In this paper, we propose the method to extract bone in DEXA image using calibration parameter based on anatomic attenuation coefficient. The experimental results reveal that the proposed method is effective.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.829-840
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• 2013

In this paper, we propose a new lung segmentation method for chest x-ray images which can take both global and local properties into account. Firstly, the initial lung segmentation is computed by applying the active shape model (ASM) which keeps the shape of deformable model from the pre-learned model and searches the image boundaries. At the second segmentation stage, we also applied the localizing region-based active contour model (LRACM) for correcting various regional errors in the initial segmentation. Finally, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by a radiologist. The comparison experiments were performed using 5 lung x-ray images. In our experiment, the Dice coefficient with manually segmented area was $95.33%{\pm}0.93%$ for the proposed method. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for a more accurate early diagnosis and prognosis regarding lung cancer in chest x-ray images.