Multi-modal medical image registration provides complementary information from the fusion of various medical imaging modalities. This paper presents a volume based multi-modal affine registration algorithm to register images acquired using different magnetic resonance imaging (MRI) modes. In the proposed algorithm, the sum-of-conditional variance (SCV) similarity measure is used. The SCV is considered to be a state-of-the- art similarity measure for registering multi-modal images. However, the main drawback of the SCV is that it uses only quantized information to calculate a joint histogram. To overcome this limitation, we propose to use partial volume interpolation (PVI) in the joint histogram calculation to improve the performance of the existing registration algorithm. To evaluate the performance of the registration algorithm, different similarity measures were compared in conjunction with gradient-based Gauss-Newton (GN) optimization to optimize the spatial transformation parameters. The experimental evaluation shows that the proposed approach provides a higher success rate and comparable accuracy to other methods that have been recently proposed for multi-modal medical image registration.