The main objective of image pre-processing is to improve the quality of an image so that it makes subsequent phases of image analysis like segmentation or recognition easier or more effective. Filtering is a key pre-processing technique used for various effects including contrast stretching, sharpening and smoothing. In this paper, we evaluate and analyse the effect of several image filtering techniques with respect to their computer aided diagnosis (CAD) performance. The techniques we investigate include contrast stretching, convolution, median fitlering, averaging, inverse transformation and logarithm transformation filters. An application of CT liver imaging CAD was chosen and the selected filters were applied to see their ability and accuracy to segment and isolate the liver region of interest using a region growing segmentation approach. The effect of the filtering techniques on the segmentation performance of the CAD system was investigated using mean squared error (MSE) and similarity index (SI). The highest performance was achieved for a contrast stretching filter (MSE = 0.1869, SI = 0.8423) and the combination of contrast stretching and average filter (MSE = 0.17198 and SI = 0.83257).