Postoperative respiratory difficulty due to asymptomatic anterior cervical osteophyte after brain tumor surgery: a case report / 대한마취과학회지

Anterior cervical osteophytes are commonly found in elderly patients, but rarely produce symptoms. When symptoms occur, they can range from mild symptoms of dysphagia, dysphonia, and foreign body sensation to severe symptoms of airway obstruction due to compression of the pharynx or larynx. We report the case of a 59-year-old man who underwent brain tumor surgery, and developed post-operative respiratory difficulty due to progressive pharyngo-laryngeal edema, requiring urgent endotracheal intubation, secondary to the presence of a previously asymptomatic anterior cervical osteophyte. It is paramount to recognize that asymptomatic anterior cervical osteophytes are a potential cause of life-threatening post-operative respiratory complications that can rapidly progress to life-threatening airway obstruction after surgeries in the prone position, especially in elderly patients.

3D Visualization of 2D CT Bone Image Using FSTB Algorithm / 대한의료정보학회지

In this paper, we propose 2D segmentation algorithm for 3D visualization using CT bone images. Kmeans algorithm and thresholding method are traditional methods that can detect human bone. But the noise appeared on segmented images generated by both Kmeans algorithm and thresholding method. To detect bone cluster, the two assumptions are considered, they are as follows; first, pixels that have high intensity can be merged into bone cluster. Secondly, because a bone cluster consists of large or small group, the ambiguous pixels can be merged into bone cluster by estimating segmentation function. With the proposed FSTB(Flexible Segmentation To Boundary) algorithm the noise didn't appeared on segmented CT bone images and the algorithm carried out effective segmentation to the ambiguous pixels.

Detection and 3D Reconstruction of Brain from MR Images / 대한의료정보학회지

In this paper, we propose a brain detection algorithm of cross-sectional images through a 3D volume. The proposed brain detection algorithm uses several steps. They are as follows; In the first step, the standard value and downward from input image data are removed. in the second step, the pixels with maximum intensity are removed but undesirable many small areas were appeared as by-products. In order to detect brain, these small areas need to be removed. In the third step, many small areas are removed by masking but some small areas still remained. In the fourth step, they are removed using three-dimensional connectivity. The proposed algorithm was applied to real human MRI data and the brain area was successfully detected.