Accurate detection of the human metaphase chromosome centromere is an important step in many chromosome analysis and medical diagnosis algorithms. The centromere location can be utilized to derive information such as the chromosome type, polarity assignment, etc. Methods available in the literature yield unreliable results mainly due to high variability of morphology in metaphase chromosomes and boundary noise present in the image. In this paper, we have proposed a multistaged algorithm which includes the use of discrete curve evolution, gradient vector flow active contours, functional approximation of curve segments, and support vector machine classification. The standard Laplacian thickness measurement algorithm was enhanced to incorporate both contour information as well as intensity information to obtain a more accurate centromere location. In addition to segmentation and width profile measurement, the proposed algorithm can also correct for sister chromatid separation in cell images. The proposed method was observed to be more accurate and statistically significant as compared to a centerline-based method when tested with 226 human metaphase chromosomes.