Title : ( Improving the quality of image segmentation in Ultrasound images using Reinforcement Learning )
Authors: Shazan Ghajari , Mohammad Bagher Naghibi Sistani ,
Full TextAbstract
Ultrasound imaging is one of the imaging techniques in medicine in which the images resulted from ultrasonic wave propagation are used for diagnostic applications. Digital processing of these images for reasons is a difficult process due to causes such as noise, same distribution of intensity values, and unknown border of organs from image tissue. In this study, image segmentation and improving image and improving quality of the image segmentation was done during three stages: preprocessing, processing and postprocessing. Multi-agent dimensional structure is selected which has the best result in the image segmentation. In the processing reinforcement learning factor, threshold operator values and opening operator dimensions obtained for image segmentation. In post-processing process, for improving the quality of image segmentation, the most appropriate dimensions are determined for morphology operators. The results of implementing the presented method on the sample of the prostate is displayed.
Keywords
, Reinforcement learning, thresholding, Multi-agent structure, Image Segmentation, Morphological operator, Q-Learning
@article{paperid:1087691,
author = {Ghajari, Shazan and Naghibi Sistani, Mohammad Bagher},
title = {Improving the quality of image segmentation in Ultrasound images using Reinforcement Learning},
journal = {Communications on Advanced Computational Science with Applications},
year = {2017},
volume = {2017},
number = {1},
month = {January},
issn = {2196-2499},
pages = {33--40},
numpages = {7},
keywords = {Reinforcement learning; thresholding; Multi-agent structure; Image Segmentation; Morphological
operator; Q-Learning},
}
%0 Journal Article
%T Improving the quality of image segmentation in Ultrasound images using Reinforcement Learning
%A Ghajari, Shazan
%A Naghibi Sistani, Mohammad Bagher
%J Communications on Advanced Computational Science with Applications
%@ 2196-2499
%D 2017
