A computer-implemented method of locating a portion of interest in image or matrix data to be analyzed, which data are derived from an item under consideration, the method comprising: obtaining approximate coordinates of a feature in training data, extending in an xy plane, which is deemed to correspond to the portion of interest, which training data have been acquired by a device which also acquired the data to be analyzed and relate to an item similar to the item under consideration, wherein the training data comprises at least two separate pieces of data and obtaining the approximate coordinates includes calculating a mean y coordinate for each x coordinate of the said feature in each piece of training data and averaging the mean y coordinates for each x coordinate; specifying a bounding region which extends around an area defined by the approximate coordinates of the feature; identifying in the data to be analyzed at least one candidate part for a component of the portion of interest; removing from the data to be analyzed any candidate parts having coordinates which fall outside the bounding region to obtain masked data; and deriving an estimate for the location of the portion of interest in the data to be analyzed from the masked data by either: (i) determining the mean y coordinate for each x coordinate of the masked data, fitting a polynomial curve of best fit to the mean coordinates, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest; or (ii) fitting a polynomial curve of best fit to the masked data, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest. obtaining approximate coordinates of a feature in training data, extending in an xy plane, which is deemed to correspond to the portion of interest, which training data have been acquired by a device which also acquired the data to be analyzed and relate to an item similar to the item under consideration, wherein the training data comprises at least two separate pieces of data and obtaining the approximate coordinates includes calculating a mean y coordinate for each x coordinate of the said feature in each piece of training data and averaging the mean y coordinates for each x coordinate; specifying a bounding region which extends around an area defined by the approximate coordinates of the feature; identifying in the data to be analyzed at least one candidate part for a component of the portion of interest; removing from the data to be analyzed any candidate parts having coordinates which fall outside the bounding region to obtain masked data; and deriving an estimate for the location of the portion of interest in the data to be analyzed from the masked data by either: (i) determining the mean y coordinate for each x coordinate of the masked data, fitting a polynomial curve of best fit to the mean coordinates, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest; or (ii) fitting a polynomial curve of best fit to the masked data, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest. (i) determining the mean y coordinate for each x coordinate of the masked data, fitting a polynomial curve of best fit to the mean coordinates, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest; or (ii) fitting a polynomial curve of best fit to the masked data, and identifying the candidate parts which fall within a region enclosing the polynomial curve of best fit as structures forming the portion of interest. <EOS>