Three-dimensional expansion of a dynamic programming method for boundary detection and its application to sequential magnetic resonance imaging (MRI).

Da-Chuan Cheng,Jui-Teng Lin

doi:10.3390/s120505195

Abstract

This study proposes a fast 3D dynamic programming expansion to find a shortest surface in a 3D matrix. This algorithm can detect boundaries in an image sequence. Using phantom image studies with added uniform distributed noise from different SNRs, the unsigned error of this proposed method is investigated. Comparing the automated results to the gold standard, the best averaged relative unsigned error of the proposed method is 0.77% (SNR = 20 dB), and its corresponding parameter values are reported. We further apply this method to detect the boundary of the real superficial femoral artery (SFA) in MRI sequences without a contrast injection. The manual tracings on the SFA boundaries are performed by well-trained experts to be the gold standard. The comparisons between the manual tracings and automated results are made on 16 MRI sequences (800 total images). The average unsigned error rate is 2.4% (SD = 2.0%). The results demonstrate that the proposed method can perform qualitatively better than the 2D dynamic programming for vessel boundary detection on MRI sequences.

Highlights

Many image edge detection methods have been proposed in the last two decades [1,2]
We propose a 3D-expansion of Dynamic programming (DP) that can seek an optimal surface in a 3D
We found that parameter s does not affect accuracy too much but the image-resize factor does impact accuracy

Summary

Introduction

Many image edge detection methods have been proposed in the last two decades [1,2]. They mainly differ in the types of smoothing filters that are applied and how the edge strength measures are computed. The artery is visible if the blood flow velocity is large enough that it appears in contrast compared to the surrounding tissue. This is most often an MRA image sequence. If the blood flow velocity is small during a short time period, the artery has limited contrast and is not visible Under this extreme situation, the 2D DP fails to detect its boundary because it is hard to obtain a feature, normally the gray-level gradient, to represent the boundary. Our previous study has applied the local contrast to add additional information to handle extreme cases [10] It still used local information but not information between two succeeding images

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