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Recent years have witnessed the emergence of 3D medical imaging techniqueswith the development of 3D sensors and technology. Due to the presence of noisein image acquisition, registration researchers focused on an alternative way torepresent medical images. An alternative way to analyze medical imaging is byunderstanding the 3D shapes represented in terms of point-cloud. Though in themedical 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 samplesfor medical images are small, researchers have used pre-trained models tofine-tune on medical images. Furthermore, due to different modality in medicalimages, standard generative models can not be used to generate new samples ofmedical images. In this work, we use the advantage of point-cloudrepresentation of 3D structures of medical images and propose a Gaussianmixture model-based generation scheme. Our proposed method is robust tooutliers. Experimental validation has been performed to show that the proposedscheme can generate new 3D structures using interpolation techniques, i.e.,given two 3D structures represented as point-clouds, we can generatepoint-clouds in between. We have also generated new point-clouds for subjectswith and without dementia and show that the generated samples are indeedclosely matched to the respective training samples from the same class.

A GMM based algorithm to generate point-cloud and its application to neuroimaging