机器学习方法提高MRI图像在肿瘤检测和诊断中的质量:基于粒子群的聚类分析

V. Sivakumar, N. Janakiraman, S. Naganandhini
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引用次数: 5

摘要

分割是图像处理中最重要、最基本的技术,用于从给定图像中提取可疑区域。脑肿瘤是在晚期通过核磁共振成像诊断出来的。本研究旨在利用计算方法量化MRI人脑扫描的脑肿瘤损失。该方法采用粒子群算法(Particle Swarm Optimization, PSO)寻找质心值进行脑组织分割。分段脑MRI帮助放射科医生检测脑异常和肿瘤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Approach improves the Quality of the MRI Images in Tumor Detection and Diagnosis: A PSO based Cluster Analysis
Segmentation is the most important and basic technique of image processing which is used for the extraction of suspicious region from the given image. Brain tumor is diagnosed at advanced stages with help of the MRI images. This research aims to quantify the brain tumor loss in MRI human Head Scans by using a computational method. This method proposes Particle Swarm Optimization (PSO) for finding the centroid value to segment the brain tissue. The segmented brain MRI helps the radiologist in detecting brain abnormalities and tumor.
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