# The Coherent Point Drift for Clustered Point Sets

## TL;DR

This paper introduces an extension to the Coherent Point Drift algorithm that incorporates prior clustering information to improve non-rigid point set registration accuracy, particularly in medical imaging applications.

## Contribution

It extends the probabilistic framework of CPD to include clustering priors via a Gaussian mixture model, enhancing registration accuracy without significant performance loss.

## Key findings

- Improved registration accuracy with clustering priors.
- Effective for medical data, especially heart model personalization.
- Comparable or better than existing methods.

## Abstract

The problem of non-rigid point set registration is a key problem for many computer vision tasks. In many cases the nature of the data or capabilities of the point detection algorithms can give us some prior information on point sets distribution. In non-rigid case this information is able to drastically improve registration results by limiting number of possible solutions. In this paper we explore use of prior information about point sets clustering, such information can be obtained with preliminary segmentation. We extend existing probabilistic framework for fitting two level Gaussian mixture model and derive closed form solution for maximization step of the EM algorithm. This enables us to improve method accuracy with almost no performance loss. We evaluate our approach and compare the Cluster Coherent Point Drift with other existing non-rigid point set registration methods and show it's advantages for digital medicine tasks, especially for heart template model personalization using patient's medical data.