Virtual simulation of the postsurgical cosmetic outcome in patients with pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome.Fundação para a Ciência e a Tecnologia, Portugal (FCT) through the Postdoc grant referenced SFRH/BPD/46851/2008 and R&D project referenced PTDC/SAU-BEB/103368/2008

Character Recognition by Levenberg-Marquardt (L-M) Algorithm Using Back Propagation ANN

The Author dedicatedly emphasis the character recognition that is applied vigorously on various techniques and the comparison of analysis has been done to justify the Network. Basis of complexity of task the network algorithm has been designed and developed and the recognition pattern is trained. The character recognition sequenced has been ranged on characters data and available technique. The emphasis has been given on the comparison and to increase the recognition accuracy and decreasing the recognition time. The character recognition interface includes the recognition of defined characters made available in the database and the integration of it. character recognition system is implemented for the characterisation of English alphabets with customised specific requirements using most contemporary optimisation algorithms ( Levenberg-Marquardt ) in back-propagation Multi layered Feed -forward network in Artificial Neural Network. The ANN training pattern has been done with most accuracy to the Characters from (A-J) are created with size of each character is n x n square matrix form