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

TL;DR

This paper introduces a Gaussian mixture model-based algorithm for generating 3D point-clouds from medical imaging data, enabling interpolation and synthesis of new structures, which is robust to noise and outliers.

Contribution

The paper presents a novel GMM-based method for generating and interpolating 3D point-clouds in medical imaging, addressing noise and modality challenges.

Findings

Successfully generated intermediate 3D structures via interpolation.

Generated realistic point-clouds for subjects with and without dementia.

Method is robust to outliers and noise in medical data.

Abstract

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.