Robust spatial information based tumour detection for brain MR images

Multimodal brain MR image analysis is still a challenging research area due to its complex intensity distribution and sensitivity towards the noise. Tumourous cells have different characteristics than normal human cells, which makes them more salient. In this Letter, the authors propose a novel unsu...

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Bibliographic Details
Published in:Electronics letters Vol. 56; no. 25; pp. 1398 - 1400
Main Authors: Maharana, A, Patra, D, Pradhan, S
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2020
Subjects:
axis ratio
biomedical MRI
brain
CIELab colour space
complex intensity distribution
computational complexity
elliptical windows
image colour analysis
input images
intensity value
medical image processing
MICCAI‐BRATS database
multimodal brain MR image analysis
normal human cells
object detection
performance analysis
pseudocoloured MR image
robust spatial information based tumour detection
saliency map
simulated brain images
spatial space
Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications
tumourous cells
tumours
unsupervised spatial information based saliency
biomedical MRI
spatial space
pseudocoloured MR image
robust spatial information based tumour detection
multimodal brain MR image analysis
complex intensity distribution
unsupervised spatial information based saliency
saliency map
object detection
brain
tumourous cells
elliptical windows
CIELab colour space
simulated brain images
intensity value
normal human cells
axis ratio
input images
image colour analysis
tumours
medical image processing
performance analysis
computational complexity
MICCAI-BRATS database
ISSN:0013-5194, 1350-911X, 1350-911X
Online Access:Get full text
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Description
Summary:Multimodal brain MR image analysis is still a challenging research area due to its complex intensity distribution and sensitivity towards the noise. Tumourous cells have different characteristics than normal human cells, which makes them more salient. In this Letter, the authors propose a novel unsupervised spatial information based saliency boosting tumour detection method which will help to identify tumourous cells by making it more clearly visible. Initially, a pseudo-coloured MR image is formed using the CIELab colour space. Saliency map has been established by calculating distance among scales varying elliptical windows in both spatial and colour space. Elliptical windows endeavour to cover-up curved outliers of the brain images. The average intensity value is kept constant by fixing the axis ratio for each window. The proposed algorithm has been evaluated on both real and simulated brain images of different patients from MICCAI-BRATS database. The performance analysis of the new algorithm exhibits higher accuracy with a low computational complexity as compared to other state of the art. The efficacy is due to the immobility of the window across rows and columns to move over the image. The novelty of the proposed technique is that neither it downscales the input images nor require any training bases.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2020.2703