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Multiple Sclerosis lesions detection by a hybrid Watershed-Clustering algorithm.

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.

journal_name

Clin Imaging

journal_title

Clinical imaging

authors

Bonanno L,Mammone N,De Salvo S,Bramanti A,Rifici C,Sessa E,Bramanti P,Marino S,Ciurleo R

doi

10.1016/j.clinimag.2020.11.006

subject

Has Abstract

pub_date

2020-11-05 00:00:00

pages

162-167

eissn

0899-7071

issn

1873-4499

pii

S0899-7071(20)30429-0

journal_volume

72

pub_type

杂志文章

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