1.A case of spargonosis in the chest wall.
Sang Ik KIM ; Young Chul YOO ; Chien Hwa YU ; Hong Sup LEE ; Chang Ho KIM ; Shin Kwang KHANG
The Korean Journal of Thoracic and Cardiovascular Surgery 1992;25(11):1240-1244
No abstract available.
Thoracic Wall*
;
Thorax*
2.The management of costochondritis of the chest wall.
Young Jin SHIN ; Taik Jong LEE
Journal of the Korean Society of Plastic and Reconstructive Surgeons 1992;19(1):67-72
No abstract available.
Thoracic Wall*
;
Thorax*
3.Arterio-venous malformation in the chest wall: a case report.
Yun Young CHOI ; Kyo Nam KIM ; Heung Suk SEO
Journal of the Korean Radiological Society 1991;27(6):796-798
No abstract available.
Thoracic Wall*
;
Thorax*
4.Surgical management of chest wall tumors.
Kyeh Hyeon PARK ; Ki Bong KIM ; Sook Whan SUNG ; Joo Hyun KIM
The Korean Journal of Thoracic and Cardiovascular Surgery 1991;24(6):547-554
No abstract available.
Thoracic Wall*
;
Thorax*
5.Reconstruction of Chest Wall Defects Using a Technique Involving Mesh, Titanium Plates, and a Pedunculated Muscle Flap.
Dave KOOLE ; Michael BEMELMAN ; Joost SCHIJEN ; Marnix DE FIJTER ; Joël VAN DER NIET
The Korean Journal of Thoracic and Cardiovascular Surgery 2018;51(5):308-311
We herein present a new surgical reconstruction technique for large chest wall defects after resection of advanced chest wall tumors.
Thoracic Wall*
;
Thorax*
;
Titanium*
6.Chest Wall Fibromatosis in the Axilla.
Seung Hyun LEE ; Hye Kyung LEE ; Ji Sun SONG ; Hii Sun JEONG
Archives of Plastic Surgery 2012;39(2):175-177
No abstract available.
Axilla
;
Fibroma
;
Thoracic Wall
;
Thorax
7.Extraskeletal Ewing's sarcoma arising in the chest wall.
The Korean Journal of Thoracic and Cardiovascular Surgery 1992;25(10):1107-1111
No abstract available.
Sarcoma, Ewing*
;
Thoracic Wall*
;
Thorax*
8.Clinical Study of Primary Chest Wall Tumors.
Chang Gon KIM ; Ja Hong KUH ; Kong Soo KIM
The Korean Journal of Thoracic and Cardiovascular Surgery 1998;31(2):155-161
Between January 1979 and August 1996, resection of a primary chest wall tumor was done in 51 patients. The mean age of the patients was 36.1 years (2 to 69 years). A palpable mass was the most common symptom (32 patients, 62.7%). The tumor was malignant in 11 patients (21.6%) and benign in 40 patients (78.4%). The tumors in 32 patients (62.7%) had developed from the bony or the cartilaginous wall and in 19 patients (37.3%) from soft tissue. Thirty seven of the patients with benign tumors were treated by excision (three of the patients: wide resection and reconstruction) without recurrence or death, and they are currently free from disease. Most malignancies (8 patients) were treated by wide resection and chest wall reconstruction. Five of them are currently alive. The chest wall reconstruction with Marlex mesh, Prolene mesh, or Teflon felt was done in five of the patients with malignant tumors.
Humans
;
Polypropylenes
;
Polytetrafluoroethylene
;
Recurrence
;
Thoracic Wall*
;
Thorax*
9.Intravascular Papillary Endothelial Hyperplasia of the Chest Wall Misdiagnosed as a Malignancy on Fine Needle Aspiration.
Yoo Duk CHOI ; Young KIM ; Sung Sun KIM ; Jo Heon KIM ; Jong Hee NAM ; Chan CHOI ; Chang Soo PARK
Korean Journal of Pathology 2013;47(5):499-501
No abstract available.
Biopsy, Fine-Needle*
;
Hyperplasia*
;
Thoracic Wall*
;
Thorax*
10.Automatic Initialization Active Contour Model for the Segmentation of the Chest Wall on Chest CT.
Healthcare Informatics Research 2010;16(1):36-45
OBJECTIVES: Snake or active contours are extensively used in computer vision and medical image processing applications, and particularly to locate object boundaries. Yet problems associated with initialization and the poor convergence to boundary concavities have limited their utility. The new method of external force for active contours, which is called gradient vector flow (GVF), was recently introduced to address the problems. METHODS: This paper presents an automatic initialization value of the snake algorithm for the segmentation of the chest wall. Snake algorithms are required to have manually drawn initial contours, so this needs automatic initialization. In this paper, our proposed algorithm is the mean shape for automatic initialization in the GVF. RESULTS: The GVF is calculated as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the medical images. Finally, the mean shape coordinates are used to automatic initialize thepoint of the snake. The proposed algorithm is composed of three phases: the landmark phase, the procrustes shape distance metric phase and aligning a set of shapes phase. The experiments showed the good performance of our algorithm in segmenting the chest wall by chest computed tomography. CONCLUSIONS: An error analysis for the active contours results on simulated test medical images is also presented. We showed that GVF has a large capture range and it is able to move a snake into boundary concavities. Therefore, the suggested algorithm is better than the traditional potential forces of image segmentation.
Diffusion
;
Snakes
;
Thoracic Wall
;
Thorax
;
Vision, Ocular
Result Analysis
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