1. State of the art survey on MRI brain tumor segmentation
- Author
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Eduard Montseny, Pilar Sobrevilla, Nelly Gordillo, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada II, and Universitat Politècnica de Catalunya. ICAIB - Grup de Recerca en Intel ligència Computacional per a l'Anàlisi d'Imatge Biomèdica
- Subjects
Pathology ,medicine.medical_specialty ,Ciències de la salut::Medicina [Àrees temàtiques de la UPC] ,Biomedical Engineering ,Biophysics ,Brain tumor ,Models, Biological ,Sensitivity and Specificity ,Pattern Recognition, Automated ,White matter ,Imaging, Three-Dimensional ,Artificial Intelligence ,Cervell -- Tumors ,Image Interpretation, Computer-Assisted ,medicine ,Medical imaging ,Animals ,Humans ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Mri brain ,Cervell -- Càncer ,Enginyeria biomèdica::Electrònica biomèdica [Àrees temàtiques de la UPC] ,Models, Statistical ,medicine.diagnostic_test ,business.industry ,Brain Neoplasms ,Brain ,Reproducibility of Results ,Magnetic resonance imaging ,Pattern recognition ,medicine.disease ,Image Enhancement ,Magnetic Resonance Imaging ,medicine.anatomical_structure ,Fully automatic ,Artificial intelligence ,Brain--Tumors ,Segmentation MRI Brain tumor ,Càncer -- Tractament ,business ,Algorithms ,Tumor segmentation - Abstract
Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema,and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.
- Published
- 2012