Lumen boundary detection using neutrosophic c-means in IVOCT images
This addresses coronary artery disease diagnosis by improving lumen boundary detection, but it appears incremental as it applies a modified clustering method to a specific medical imaging task.
The paper tackled lumen boundary detection in intravascular optical coherence tomography images using neutrosophic c-means clustering, achieving high accuracy with metrics such as a Jaccard measure of 0.972 and a dice index of 0.985.
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.