research-article

An interactive medical image segmentation framework using iterative refinement

Authors:

Pratik Kalshetti,

Parag Rahangdale,

Chiranjoy Chattopadhyay,

Abhay ElhenceAuthors Info & Claims

Computers in Biology and Medicine, Volume 83, Issue C

Pages 22 - 33

https://doi.org/10.1016/j.compbiomed.2017.02.002

Published: 01 April 2017 Publication History

Abstract

Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Graphical abstractDisplay Omitted

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An interactive medical image segmentation framework using iterative refinement
1. Computing methodologies
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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 83, Issue C

April 2017

182 pages

ISSN:0010-4825

Issue’s Table of Contents

Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2017

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Cited By

Fan XQiao XWang ZJiang LLiu YSun Q(2022)Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel TreatmentComputational Intelligence and Neuroscience10.1155/2022/36886302022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3688630
Cheng CWang JLi HWang L(2021)Optimal Atlas Segmentation on CT Images for Diagnosis of Pediatric Mycoplasma PneumoniaScientific Programming10.1155/2021/25869562021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2586956
Rajput ATanwar VRaman B(2021)-Score-Based Secure Biomedical Model for Effective Skin Lesion Segmentation Over eHealth CloudACM Transactions on Multimedia Computing, Communications, and Applications10.1145/343080617:2s(1-19)Online publication date: 14-Jun-2021
https://dl.acm.org/doi/10.1145/3430806
Alzahrani YBoufama B(2021)Biomedical Image Segmentation: A SurveySN Computer Science10.1007/s42979-021-00704-72:4Online publication date: 30-May-2021
https://dl.acm.org/doi/10.1007/s42979-021-00704-7
Palani DVenkatalakshmi K(2019)An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and ClassificationJournal of Medical Systems10.1007/s10916-018-1139-743:2(1-12)Online publication date: 1-Feb-2019
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Zhang WWang XZhang PChen J(2017)Global optimal hybrid geometric active contour for automated lung segmentation on CT imagesComputers in Biology and Medicine10.1016/j.compbiomed.2017.10.00591:C(168-180)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.compbiomed.2017.10.005

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