Multi-threshold image segmentation based on an improved differential evolution: Case study of thyroid papillary carcinoma

The scholarly world has demonstrated an immense enthusiasm for medical image segmentation due to its intricate nature and critical role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, due to its simplicity and straightforwardness. This paper presents an improved Differential Evolution (DE) algorithm called AGDE, which is based on MTIS and was used to evaluate its high capability at IEEE CEC 2017. Comparisons with classical and advanced algorithms were conducted as part of the experiments. An AGDE-based multi-threshold image segmentation method utilizing a non-local mean 2D histogram in combination with Re ' nyi's entropy was applied to segment images from the Berkeley Segmentation Datasets 500 (BSDS500) and microscopic images of thyroid papillary carcinoma (TPC). The experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.

Automated summary

The scholarly world has shown great interest in medical image segmentation due to its complex nature and important role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, known for its simplicity and straightforwardness. This paper introduces an improved Differential Evolution (DE) algorithm called AGDE, based on MTIS, which was used to evaluate its high performance at IEEE CEC 2017. Experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.