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Electrotherapy is a treatment for various conditions, and can be difficult to quantify in effect. This paper examines the form known as interferential therapy, with particular application in the management of urinary incontinence. Objective validation of the treatment is described, which allows optimal positioning of the electrodes for a particular patient.

Several algorithms have been defined which can segment images, each algorithm having its own merits. The Maximum Likelihood (ML) algorithm is considered the most accurate, while the Fuzzy c-Means (FCM) algorithm converges more quickly. This paper describes a generalisation of the FCM algorithm (GFCM) which is more versatile than the standard FCM, having discriminant functions which may, by changing parameters, be varied in order to suit particular applications.The discriminant functions can thus be more realistic than those used in the standard FCM and in the limiting case they approach gaussians, where the algorithm produces results identical to some implementations of ML.

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