Segmentation technique for medical image processing: A survey

Title

Segmentation technique for medical image processing: A survey

Creator

Merjulah R.; Chandra J.

Description

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. 2017 IEEE.

Source

Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017, pp. 1055-1061.

Date

2018-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Computer Tomography (CT); Convolutional neural Network (CNN); Deep learning (DL); Machine learning (ML); Magnetic Resonance Imaging (MRI); Positron Emission Tomography (PET)

Coverage

Merjulah R., Computer Science, Christ University, Bangalore, India; Chandra J., Computer Science, Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-153864031-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICICI.2017.8365301

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