Highlights
- •We developed a CAD system by a modified algorithm for automated image segmentation.
- •Algorithm is able to segment lesions in FLAIR images with a good accuracy.
- •The automatic algorithm may discriminate Multiple Sclerosis lesions from non-lesions.
Abstract
Background
Computer Aided Diagnosis (CAD) systems have been developing in the last years with
the aim of helping the diagnosis and monitoring of several diseases. We present a
novel CAD system based on a hybrid Watershed-Clustering algorithm for the detection
of lesions in Multiple Sclerosis.
Methods
Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients
affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system
consisted of the following automated processing steps: images recording, automated
segmentation based on the Watershed algorithm, detection of lesions, extraction of
both dynamic and morphological features, and classification of lesions by Cluster
Analysis.
Results
The investigation was performed on 316 suspect regions including 255 lesion and 61
non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly
significant difference between lesions and non-lesions; the diagnostic accuracy was
87% (95% CI: 0.83–0.90), with an appropriate cut-off of 192.8; the sensitivity was
77% and the specificity was 87%.
Conclusions
In conclusion, we developed a CAD system by using a modified algorithm for automated
image segmentation which may discriminate MS lesions from non-lesions. The proposed
method generates a detection out-put that may be support the clinical evaluation.
Keywords
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Article info
Publication history
Published online: November 05, 2020
Accepted:
November 2,
2020
Received in revised form:
September 21,
2020
Received:
March 24,
2020
Identification
Copyright
© 2020 Elsevier Inc. All rights reserved.
