Local mean based adaptive thresholding to classify the cartilage and background superpixels
Semi-automatic segmentation is common in medical image processing because anatomical geometries demonstrated by human anatomical parts often requires manual supervision to provide desirable results. However, semi-automatic segmentation has been infamous for requiring excessive human intervention and...
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| Main Authors: | , , , , , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2019-07-01.
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| Online Access: | Get fulltext |
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| LEADER | 02878 am a22003733u 4500 | ||
|---|---|---|---|
| 001 | ijeecs18483_12556 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Seng Gan, Hong |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Adb Salam, Bakhtiar Al-Jefri |e author |
| 700 | 1 | 0 | |a Ahmad Khaizi, Aida Syafiqah |e author |
| 700 | 1 | 0 | |a Hanif Ramlee, Muhammad |e author |
| 700 | 1 | 0 | |a Wan Mahmud, Wan Mahani |e author |
| 700 | 1 | 0 | |a Lee, Yeng-Seng |e author |
| 700 | 1 | 0 | |a Amir Sayuti, Khairil |e author |
| 700 | 1 | 0 | |a Tarmizi Musa, Ahmad |e author |
| 245 | 0 | 0 | |a Local mean based adaptive thresholding to classify the cartilage and background superpixels |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18483 | ||
| 520 | |a Semi-automatic segmentation is common in medical image processing because anatomical geometries demonstrated by human anatomical parts often requires manual supervision to provide desirable results. However, semi-automatic segmentation has been infamous for requiring excessive human intervention and time consuming. In order to reduce a forementioned problems, seed labels have been generated automatically using superpixels in our previous works. A fixed threshold method has been implemented to classify cartilage and background superpixels but this method is reported to lack the adaptiveness to changing image properties in 3D magnetic resonance image of knee. As a result, the coverage of background seeds are not sufficient to cover whole background area in some cases. In this work, we proposed a local mean based adaptive threshold method as a better alternative to the fixed threshold method. We calculated local mean for each block in an integral image and then use it to differentiate background superpixels from cartilage superpixels. The method is robust to illumination changes and simple to use. We tested the adaptive threshold on 35 knee images of different anatomical geometries and proved the proposed method could provide more comprehensive background seed labels distribution compared to fixed threshold method | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Adaptive Threshold; Knee Cartilage Segmentation; Random Walks; Seeds | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 15, No 1: July 2019; 211-220 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v15.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18483/12556 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18483/12556 |z Get fulltext |