  Recent years have witnessed the emergence of 3D medical imaging techniques
with the development of 3D sensors and technology. Due to the presence of noise
in image acquisition, registration researchers focused on an alternative way to
represent medical images. An alternative way to analyze medical imaging is by
understanding the 3D shapes represented in terms of point-cloud. Though in the
medical imaging community, 3D point-cloud processing is not a ``go-to'' choice,
it is a ``natural'' way to capture 3D shapes. However, as the number of samples
for medical images are small, researchers have used pre-trained models to
fine-tune on medical images. Furthermore, due to different modality in medical
images, standard generative models can not be used to generate new samples of
medical images. In this work, we use the advantage of point-cloud
representation of 3D structures of medical images and propose a Gaussian
mixture model-based generation scheme. Our proposed method is robust to
outliers. Experimental validation has been performed to show that the proposed
scheme can generate new 3D structures using interpolation techniques, i.e.,
given two 3D structures represented as point-clouds, we can generate
point-clouds in between. We have also generated new point-clouds for subjects
with and without dementia and show that the generated samples are indeed
closely matched to the respective training samples from the same class.
