# Breast Cancer: Model Reconstruction and Image Registration from Segmented Deformed Image using Visual and Force based Analysis

## TL;DR

This paper presents a novel method combining image segmentation, force-based surface rectification, and advanced feature matching to accurately reconstruct and register breast surface images, aiding in tactile imaging for breast lesion localization.

## Contribution

It introduces a new integrated approach for reconstructing deformed breast surfaces using force correction and affine-invariant features, improving accuracy over existing methods.

## Key findings

- Achieved 99.7% positioning accuracy in path reconstruction.
- Validated the model on theoretical and real scenarios.
- Demonstrated advantages over competing methods.

## Abstract

Breast lesion localization using tactile imaging is a new and developing direction in medical science. To achieve the goal, proper image reconstruction and image registration can be a valuable asset. In this paper, a new approach of the segmentation-based image surface reconstruction algorithm is used to reconstruct the surface of a breast phantom. In breast tissue, the sub-dermal vein network is used as a distinguishable pattern for reconstruction. The proposed image capturing device contacts the surface of the phantom, and surface deformation will occur due to applied force at the time of scanning. A novel force based surface rectification system is used to reconstruct a deformed surface image to its original structure. For the construction of the full surface from rectified images, advanced affine scale-invariant feature transform (A-SIFT) is proposed to reduce the affine effect in time when data capturing. Camera position based image stitching approach is applied to construct the final original non-rigid surface. The proposed model is validated in theoretical models and real scenarios, to demonstrate its advantages with respect to competing methods. The result of the proposed method, applied to path reconstruction, ends with a positioning accuracy of 99.7%