Gout is a disease of antiquity but is increasing once again in prevalence despite availability of reasonably effective treatments. This may be related to a combination of factors, including diet, obesity, and diuretic use. Allergic reactions, noncompliance, drug interactions, and sometimes inefficacy all limit the effective use of current hypouricemic agents. The objective of this paper is to show  that for every color space there exists an optimum Gout skin detector scheme so that the performance of all these skin detectors schemes is the same, and then process the Random segmentation blocks  algorithm in order to recognition Gout skin  A theoretical proof is provided and experiments are presented which show that the separability of the skin and no skin classes is independence of color space and some parameters chosen (experimentation  for 80 gout image for different types and 80 other Dermatological disorders images) for testing are Energy, Entropy,  Average and  Variance. 160 patients were randomly placed in three groups and treated topically along 7-weeks with either gout in foot or hand or other parts body. The recognition results for testing program by Random segmentation blocks algorithm shows superior efficacy for gout skin detection (the testing stage contain all 160 images to recognized only gouts images).