Interval type-2 fuzzy predicates for brain magnetic resonance image segmentation

• Autores: Comas, Diego Sebastián; Meschino, Gustavo Javier; Costantino, Sebastián; Capiel, Carlos; Ballarin, Virginia Laura
• Año de publicación: 2017
• Idioma: español castellano
• Tipo de recurso: artículo
• Materia: Volumen encefálico; Predicados difusos; Lógica difusa tipo 2 de intervalos; resonancia magnética
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.
The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.
Fil: Comas, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; Argentina
Fil: Meschino, Gustavo Javier. Universidad FASTA ; Argentina
Fil: Costantino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; Argentina
Fil: Capiel, Carlos. Instituto Radiologico Mar del Plata; Argentina. Universidad FASTA ; Argentina
Fil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; Argentina. Universidad FASTA ; Argentina