Gray Level Image Threshold Using Neutrosophic Shannon Entropy

Vasile Patrascu

doi:10.20944/preprints201906.0248.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 23 June 2019 / Approved: 25 June 2019 / Online: 25 June 2019 (08:48:22 CEST)

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.

image segmentation; neutrosophic information; Shannon entropy; gray level image threshold

Computer Science and Mathematics, Computer Vision and Graphics

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Gray Level Image Threshold Using Neutrosophic Shannon Entropy

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