Segmentation Technique for Medical Image Processing: A Survey

被引：0

作者：

Merjulah, R. ^{[1
]}

Chandra, J. ^{[1
]}

机构：

[1] Christ Univ, Comp Sci, Bangalore, Karnataka, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTING AND INFORMATICS (ICICI 2017) | 2017年

关键词：

Machine learning (ML); Deep learning (DL); Convolutional neural Network (CNN); Magnetic Resonance Imaging (MRI); Computer Tomography (CT); Positron Emission Tomography (PET); CONVOLUTIONAL NEURAL-NETWORKS; LEFT-VENTRICLE; HEART; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis.

引用

页码：1055 / 1061

页数：7

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