1. A computer implemented method for processing a block to be processed of a urine sediment image by a processor executing executable instructions stored in a non-transitory memory, the method comprising: calculating a variable number of local feature vectors of the block to be processed, wherein the local feature vector is a vector representing a feature of a local location of the block to be processed, the variable number of local feature vectors comprise m scale invariant feature transform (SIFT) feature vectors and n local binary pattern (LBP) feature vectors, where m and n are variable positive integers, wherein the m SIFT feature vectors of the block to be processed are calculated as follows: applying a successive Gaussian filter to the block to be processed a plurality of times and/or a scaled block to be processed so as to obtain a multi-layer block; by calculating a difference of various pixel values corresponding to an adjacent-layer block in the multi-layer block, obtaining a Gaussian difference block between adjacent-layer blocks so as to form a multi-layer Gaussian difference block; with regard to a specific pixel in a specific-layer Gaussian difference block in the multi-layer Gaussian difference block, judging whether the value of the specific pixel is a maximum pixel value or a minimum pixel value in a 3-pixel×3-pixel×3-pixel cube with the specific pixel as the center of the specific-layer Gaussian difference block, an upper-layer Gaussian difference block and a lower-layer Gaussian difference block, and if yes, marking the location of the specific pixel as a key point, wherein with regard to the multi-layer Gaussian difference block, m key points are marked altogether; calculating a gradient value and a gradient direction of a pixel in a specific adjacent area in a plurality of adjacent areas of the key point, wherein the gradient direction is approximated to one of a pre-specified plurality of standard directions; and calculating a total gradient value of a pixel in the specific adjacent area of the key point in a specific standard direction to be taken as a component of the SIFT feature vector of the key point in the specific adjacent area in the specific standard direction; classifying the m SIFT feature vectors into k1 clusters and classifying the n LBP feature vectors into k2 clusters so as to obtain a statistical histogram vector of the k1 clusters of the SIFT feature vectors and a statistical histogram vector of the k2 clusters of the LBP feature vectors, where k1 and k2 are fixed positive integers; taking the statistical histogram vector as a feature in a feature set of block processing; and processing the block to be processed with the statistical histogram vector in the feature set of block processing.