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Previous issue date: 2014-09-25 / Segmenta??o digital de imagens ? o processo de atribuir r?tulos distintos a diferentes
objetos em uma imagem digital, e o algoritmo de segmenta??o fuzzy tem sido utilizado
com sucesso na segmenta??o de imagens de diversas modalidades. Contudo, o algoritmo
tradicional de segmenta??o fuzzy falha ao segmentar objetos que s?o caracterizados por
texturas cujos padr?es n?o podem ser descritos adequadamente por simples estat?sticas
computadas sobre uma ?rea restrita. Neste trabalho apresentamos uma extens?o do algoritmo
de segmenta??o fuzzy que realiza segmenta??o de texturas empregando fun??es
de afinidade adaptativas e o estendemos a imagens tridimensionais. Fun??es de afinidade
adaptativas mudam o tamanho da ?rea em que s?o calculados os descritores da textura de
acordo com as caracter?sticas da textura processada, enquanto imagens tridimensionais
podem ser descritas como um conjunto finito de imagens bidimensionais. O algoritmo ent?o
segmenta o volume com uma ?rea apropriada calculada para cada textura, tornando
poss?vel obter boas estimativas dos volumes reais das estruturas alvo do processo de segmenta??o.
Experimentos ser?o realizados com dados sint?ticos e reais obtidos no estudo
de segmenta??o de tumores cerebrais em imagens m?dicas adquiridas atrav?s de exames
de Resson?ncia Magn?tica / Digital image segmentation is the process of assigning distinct labels to different objects
in a digital image, and the fuzzy segmentation algorithm has been used successfully
in the segmentation of images from several modalities. However, the traditional fuzzy
segmentation algorithm fails to segment objects that are characterized by textures whose
patterns cannot be successfully described by simple statistics computed over a very restricted
area. In this paper we present an extension of the fuzzy segmentation algorithm
that achieves the segmentation of textures by employing adaptive affinity functions as
long as we extend the algorithm to tridimensional images. The adaptive affinity functions
change the size of the area where they compute the texture descriptors, according
to the characteristics of the texture being processed, while three dimensional images can
be described as a finite set of two-dimensional images. The algorithm then segments the
volume image with an appropriate calculation area for each texture, making it possible
to produce good estimates of actual volumes of the target structures of the segmentation
process. We will perform experiments with synthetic and real data in applications such
as segmentation of medical imaging obtained from magnetic rosonance
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/19668 |
| Date | 25 September 2014 |
| Creators | Silva Neto, Jos? Francisco da |
| Contributors | 79228860472, http://lattes.cnpq.br/0330924133337698, Santos, Selan Rodrigues dos, 47337761368, http://lattes.cnpq.br/4022950700003347, Mendes Neto, Francisco Milton, 67304133449, http://lattes.cnpq.br/5725021666916341, Carvalho, Bruno Motta de |
| Publisher | Universidade Federal do Rio Grande do Norte, PROGRAMA DE P?S-GRADUA??O EM SISTEMAS E COMPUTA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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