Comparison of Hybrid ACO-k-means algorithm and Grub cut for MRI images segmentation

被引：6

作者：

El-Khatib, S. A. ^{[1
]}

Skobtsov, Y. A. ^{[2
]}

Rodzin, S., I ^{[1
]}

机构：

[1] Southern Fed Univ, 105-42 Bolshaya Sadovaya str, Rostov Na Donu 344006, Russia

[2] St Petersburg State Univ Aerosp Instrumentat, 67 Bolshaya Morskaya Str, St Petersburg 190000, Russia

来源：

14TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS | 2021年 / 186卷

基金：

俄罗斯基础研究基金会;

关键词：

MRI image segmentation; ant colony optimization; k-means algorithm; swarm intelligence; grub cut;

D O I：

10.1016/j.procs.2021.04.150

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：316 / 322

页数：7

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