Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering.
J Healthc Eng
; 2017: 8536206, 2017.
Article
em En
| MEDLINE
| ID: mdl-29158887
We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%-93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Planejamento da Radioterapia Assistida por Computador
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Encéfalo
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Neoplasias Encefálicas
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Neuroimagem
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Healthc Eng
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Reino Unido