NDLI: A Reinforcement Learning Framework for Medical Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Sahba, F. Tizhoosh, H.R. Salama, M.M.A.
Copyright Year	2006
Description	Author affiliation: Waterloo Univ., Waterloo (Sahba, F.; Tizhoosh, H.R.)
Abstract	This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.
Starting Page	511
Ending Page	517
File Size	673016
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Biomedical imaging Image segmentation Ultrasonic imaging Medical diagnostic imaging Pattern analysis Machine intelligence Laboratories System analysis and design Design engineering
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Opposition-Based Reinforcement Learning in Image Segmentation

Medical Image Segmentation by Using Reinforcement Learning Agent

Edge-based image segmentation: machine learning from examples

Evaluation of Segmentation algorithms for Medical Imaging

A Medical Texture Local Binary Pattern For TRUS Prostate Segmentation

Learning to segment using machine-learned penalized logistic models

Quasi-global oppositional fuzzy thresholding

An enhanced appearance model for ultrasound image segmentation

Automated design of optimal border detection criteria: learning from image segmentation examples

A Reinforcement Learning Framework for Medical Image Segmentation

Similar Documents

Application of Opposition-Based Reinforcement Learning in Image Segmentation

Medical Image Segmentation by Using Reinforcement Learning Agent

Edge-based image segmentation: machine learning from examples

Evaluation of Segmentation algorithms for Medical Imaging

A Medical Texture Local Binary Pattern For TRUS Prostate Segmentation

Learning to segment using machine-learned penalized logistic models

Quasi-global oppositional fuzzy thresholding

An enhanced appearance model for ultrasound image segmentation

Automated design of optimal border detection criteria: learning from image segmentation examples

A Reinforcement Learning Framework for Medical Image Segmentation