Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI
Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Putra Malaysia Press
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/35471/1/Pseudo.pdf http://ir.unimas.my/id/eprint/35471/ http://www.pertanika.upm.edu.my/resources/files/Pertanika%20PAPERS/JST%20Vol.%2029%20(2)%20Apr.%202021/03%20JST-2213-2020.pdf https://doi.org/10.47836/pjst.29.2.03 |
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| Summary: | Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic
resonance imaging (MRI) is an essential and challenging task. Recently, there are several
numbers of related works on the automatic segmentation of infarct lesion from the input
image and give a high accuracy in extraction of infarct lesion. Still, limited works have been
reported in isolating the penumbra tissues and infarct core separately. The segmentation of
the penumbra tissues is necessary because that region has the potential to recover. This paper
presented an automated segmentation algorithm on diffusion-weighted magnetic resonance
imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering
techniques. A greyscale image contains only intensity information and often misdiagnosed
due to overlap intensity of an image. Colourization is the method of adding colours to
greyscale images which allocate luminance or intensity for red, green, and blue channels.
The greyscale image is converted to pseudo-colour is to intensify the visual perception
and deliver more information. Then, the algorithm segments the region of interest (ROI)
using K-means clustering. The result shows
the potential of automated segmentation to
differentiate between the healthy and lesion
tissues with 90.08% in accuracy and 0.89
in dice coefficient. The development of
an automated segmentation algorithm was
successfully achieved by entirely depending
on the computer with minimal interaction. |
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