# Gray Level Image Threshold Using Neutrosophic Shannon Entropy

## TL;DR

This paper introduces a novel grayscale image segmentation technique that minimizes Shannon's neutrosophic entropy, effectively determining optimal thresholds by analyzing neutrosophic information components.

## Contribution

It proposes a new segmentation method based on neutrosophic Shannon entropy, capable of multiple thresholds, with demonstrated effectiveness on test images.

## Key findings

- Good segmentation performance demonstrated
- Effective in determining optimal gray level thresholds
- Method is simple and easy to understand

## Abstract

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.