skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Review methods for image segmentation from computed tomography images

Abstract

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.

Authors:
; ;  [1];  [2]
  1. Faculty of Science Computer and Mathematics, Universiti Teknologi Mara Malaysia, 40450 Shah Alam Selangor (Malaysia)
  2. Faculty of Medicine and Health Sciences, Universiti Putra Malaysia 43400 Serdang Selangor (Malaysia)
Publication Date:
OSTI Identifier:
22390747
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1635; Journal Issue: 1; Conference: ICOQSIA 2014: 3. International Conference on Quantitative Sciences and Its Applications, Langkawi, Kedah (Malaysia), 12-14 Aug 2014; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ACCURACY; ANIMAL TISSUES; AUTOMATION; CAT SCANNING; IMAGE PROCESSING; IMAGES; NEOPLASMS; RADIOTHERAPY

Citation Formats

Mamat, Nurwahidah, Rahman, Wan Eny Zarina Wan Abdul, Soh, Shaharuddin Cik, and Mahmud, Rozi. Review methods for image segmentation from computed tomography images. United States: N. p., 2014. Web. doi:10.1063/1.4903576.
Mamat, Nurwahidah, Rahman, Wan Eny Zarina Wan Abdul, Soh, Shaharuddin Cik, & Mahmud, Rozi. Review methods for image segmentation from computed tomography images. United States. doi:10.1063/1.4903576.
Mamat, Nurwahidah, Rahman, Wan Eny Zarina Wan Abdul, Soh, Shaharuddin Cik, and Mahmud, Rozi. Thu . "Review methods for image segmentation from computed tomography images". United States. doi:10.1063/1.4903576.
@article{osti_22390747,
title = {Review methods for image segmentation from computed tomography images},
author = {Mamat, Nurwahidah and Rahman, Wan Eny Zarina Wan Abdul and Soh, Shaharuddin Cik and Mahmud, Rozi},
abstractNote = {Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.},
doi = {10.1063/1.4903576},
journal = {AIP Conference Proceedings},
number = 1,
volume = 1635,
place = {United States},
year = {Thu Dec 04 00:00:00 EST 2014},
month = {Thu Dec 04 00:00:00 EST 2014}
}