We have developed an adaptive split-and-merge method for a medical image segmentation algorithm and investigated its parallel implementation. The key process of the segmentation method, i.e., the test of region homogeneity, is carried out by means of a localized feature analysis technique and statistical tests. The feature analysis technique combines a co-occurrence matrix and the histogram of its near-diagonal elements for calculating the threshold values of the average standard deviation, gray-level contrast, and likelihood ratio. We then use these values as constraints in the statistical hypothesis tests to determine whether two regions should be split or merged in the final region formation. The strength of the proposed method is that all the required parameters in the algorithm are computed automatically and depend only on the context of the image under analysis. The calculation of the feature measurements in this algorithm is window-based; the value computed for each pixel is a function of its neighboring pixels, the computation time is enormous and is inherently suitable for parallel implementation. We have incorporated the proposed algorithm by using an AT&T Pixel Machine. The parallelization can be done by division of image into 8 × 8 blocks of equal size and some boundary pixels that overlap with four neighboring pixel nodes. The preliminary result shows that the saving in computation time with this parallelized implementation is significant.