Lumen boundary detection using neutrosophic c-means in IVOCT images

TL;DR

This paper introduces a novel neutrosophic c-means clustering method for lumen boundary detection in high-resolution IVOCT images, improving accuracy in identifying coronary artery walls for disease diagnosis.

Contribution

It proposes a new clustering approach using neutrosophic theory for precise lumen boundary detection in intravascular OCT images, enhancing diagnostic accuracy.

Findings

High accuracy with Jaccard measure of 0.972

Low area difference of 0.019 mm²

Average boundary distance of 0.32 mm

Abstract

In this paper, a novel method for lumen boundary identification is proposed using Neutrosophic c_means. This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, which aims to detect the boundaries. Intravascular optical coherence tomography images are cross-sectional and high-resolution images which are taken from the coronary arterial wall. Coronary Artery Disease cause a lot of death each year. The first step for diagnosing this kind of diseases is to detect lumen boundary. Employing this approach, we obtained 0.972, 0.019, 0.076 mm2, 0.32 mm, and 0.985 as mean value for Jaccard measure (JACC), the percentage of area difference (PAD), average distance (AD), Hausdorff distance (HD), and dice index (DI), respectively. Based on our results, this method enjoys high accuracy performance.