Advanced SearchSearch Tips
Development of an algorithm for Detecting Symptom level in patients with Scleroderma
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Development of an algorithm for Detecting Symptom level in patients with Scleroderma
Jeong, Jin-Hyeong; Lee, Ki-Young; Kim, Min-yeong; Kim, Nam-Sun; Lee, Sang-Sik;
  PDF(new window)
In this study, locality of scleroderma was detected. Diagnostic method is difficult for scleroderma (skin curing; Scleroderma), and it is done by comparing the images of the normal subjects to the scleroderma patients, after performing monochrome processing. The saturation, brightness, and contrast are adjusted, and they were converted by using the process of Well Filter. As a result, the images were able to be used to clearly distinguish the symptoms of scleroderma. In addition, in a video of a healthy person, the line of sight of the observation given the image of scleroderma patients above sea level of height as is to implement the closing process to the rear Well Filter even only in so that the horizontal plane, and out at intervals of graph the amplitude difference of the video have I asked. The diagnostic criteria were determined for the healthy subjects and the scleroderma patients.
Scleroderma;Skin;Rough;Well Filter;Elevation;Length;Brightness;Saturation;
 Cited by
B. K. Walder F.R.A.C.P "Do Solvents Cause Scleroderma?" 2008 International Journal of Dermatology Volume 22, Issue 3, pages 157-158.

Cantwell, Jr A.R. . Craggs E. . Wilson J.W. . Swatek F. "Acid-Fast Bacteria as a Possible Cause of Scleroderma." 1968;136:141-150 (DOI:10.1159/000254093) crossref(new window)

Noel R. Rosea, Constantin Bonab "Defining criteria for autoimmune diseases." 2003 Immunology Today Volume 14, Issue 9, Pages 426-430

William A. D'Angelo, James F. Fries, Alfonse T. Masi. Dr.P.H. Lawrence E. Shulman, M.D., Ph.D "Pathologic observations in systemic sclerosis (scleroderma) ${\bigstar}$: A study of fifty-eight autopsy cases and fifty-eight matched controls" 1968 Volume 46, Issue 3, , Pages 428-440

LeRoy EC1, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr, Rowell N, Wollheim F." Scleroderma (systemic sclerosis): classification, subsets and pathogenesis." 1988 15(2):202-205

Arabadzhiev T. I., Dimitrov, G. V., Dimitrova, N. A., 2005, "Simulation analysis of the performance of a novel high sensituve index for quantifying M-wave spectral changes during fatigue," J. Electromyography and Kinesiology Vol. 15, pp. 149-158 crossref(new window)

Ament W, Bonga GJ, Hof AL, Verkerke GJ., 1996, "Electromyogram median power frequency in dynamic exercise at medium exercise intensities," Eun J Appl Physiol, Vol. 74, pp. 180-186 crossref(new window)

S.C. Orphanoudakis, "Supercomputing in Medical Imaging" 1988 IEEE Eng Med Biol, vol. 7, 16-20

McAuliffe, M.J. "Medical Image Processing, Analysis and Visualization in clinical research" Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on. Page 381-386.

Lee, Ki-Young. "study on symptom level for patient with Scleroderma" 2011 The Korea Institute of Information Electronic Communication Technology Vol.4 No.1 P.231-234

Carol M. Artlett, Ph, J. Bruce Smith, Sergio A. Jimenez. "Identification of Fetal DNA and Cells in Skin Lesions from Women with Systemic Sclerosis" 1998 338:1186-1191 DOI: 10.1056/NEJM199804233381704 crossref(new window)