Multiple Sclerosis lesions detection by a hybrid Watershed-Clustering algorithm

• Published in: Clinical imaging Vol. 72; pp. 162 - 167
• Main Authors: Bonanno, Lilla, Mammone, Nadia, De Salvo, Simona, Bramanti, Alessia, Rifici, Carmela, Sessa, Edoardo, Bramanti, Placido, Marino, Silvia, Ciurleo, Rosella
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2021; Elsevier Limited
• Subjects: Algorithms; CAD system; Cluster Analysis; Clustering; Diagnosis; Diagnosis, Computer-Assisted; Diagnostic systems; Feature extraction; Gadolinium; Humans; Hybrid systems; Image processing; Image segmentation; Lesions; Magnetic Resonance Imaging; Medical imaging; Multiple Sclerosis; Multiple Sclerosis - diagnostic imaging; Noise; Software; Standard deviation; Ultrasonic imaging; Watershed algorithm; Watersheds; Multiple Sclerosis; Image segmentation; Magnetic Resonance Imaging; Watershed algorithm; CAD system
• ISSN: 0899-7071; 1873-4499; 1873-4499
• DOI: 10.1016/j.clinimag.2020.11.006

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. 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. 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%. 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. •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.