1. A method comprising: automatically causing a representation of an in-vivo bundle of brain fibers to be rendered, said in-vivo bundle of brain fibers tracked via clusters, said clusters each comprising a predetermined number of particles, each particle of a particular cluster representative of a discreet path associated with said particular cluster in a tensor field, said discreet path a set of locations in said tensor field, said tensor field computed via a magnetic resonance imaging sensor, said representation comprising a most likely fiber structure, said most likely fiber structure comprising a path followed by a subset of said particles, said subset associated with particle probabilities above a predetermined threshold, each of said predetermined number of particles iteratively sampled via a probability function: p tensor ⁡ ( x t | x t - 1 ) = 1 ( 2 ⁢ π ) 3 / 2 ⁢ exp ⁡ ( - 1 2 ⁢ ( x t - x t - 1 ) T ⁢ D ⁡ ( x t - 1 ) - 1 ⁢ ( x t - x t - 1 ) ) where: D(x) is a normalized tensor at location x, said normalized tensor obtained from said magnetic resonance imaging sensor; t is a time interval associated with tracking said in-vivo bundle of brain fibers; and x is a location associated with tracking said in-vivo bundle of brain fibers; each of said predetermined number of particles having an associated weight computed from an equation: w 0 ⁢ : ⁢ t i = p ⁡ ( x t i | x t - 1 i ) p tensor ⁡ ( x t i | x t - 1 i ) ⁢ w 0 ⁢ : ⁢ ⁢ t - 1 i where: w 0:t i is a weight associated with particle i at time t; t is a time interval associated with tracking said in-vivo bundle of brain fibers; x i is a location associated with tracking said in-vivo bundle of brain fibers associated with particle i; p tensor is obtained from said probability function; 
 p ( x t |x t−1 )= p tensor ( x t |x t−1 )× p reg ( x t |x t−1 )× p fa ( x t |x t−1 ) where: p reg is a probability derived from a distance between x t−1 −x t−2 and x t −x t−1 ; and p fa is a fractional anisotropy probability computed from fractional anisotropy at location x t determined from said normalized tensor; each of said clusters created from a first particle and via recursive additions of a next particle to a closest existing cluster if a determined distance between said next particle and a center of said closest existing cluster is smaller than said predetermined threshold; and iteratively sampling each of said predetermined number of particles via said probability function.