Metodologias para detecção do centrômero no processo de identificação de cromossomos

• Ano de defesa: 2011
• Autor(a) principal: Kurtz, Guilherme Chagas
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/00130000112pr
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Ciência da Computação; UFSM; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Processamento de imagens; Cromossomos; Identificação de cromossomos; Detecção do centrômero; Reconhecimento de padrões; Image processing; Chromosomes; Chromosomes identification; Centromere detection; Pattern recognition; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/5384

Many genetic diseases or abnormalities that may occur in human chromosomes can be detected by analyzing the shape and morphology of chromosomes. The elaboration of the karyotype (organization of the 24 chromosomes of a human cell according to its size through a drawing or a photograph obtained from a microscope) is usually used to achieve this goal. The first steps for chromosomal analysis is the definition and extraction of morphology and banding pattern (gray level variations along its length) features of chromosomes. Among the morphological characteristics, in addition to its size, there is the centromere location (a region that divides the chromosome in long arm and short arm) and the classification according to the same. The advances made in cell culture techniques, banding, collecting and analyzing of materials for the implementation of the karyotype allowed great progress in the diagnosis of chromosomal abnormalities. However, this process is still used manually, because despite the growing demand of this type of examination, it is still small the supply of automated systems that help the geneticists work in the katyotype generation. So, the automation of this process and the possibility of obtaining results in a short time speeding therapeutic conduct and reassuring that families are invaluable. Centromere detection is of great importance both in the manual process as the automatic process, for faster diagnosis. In the manual process, the possibility of performing a grouping of the chromosomes in relation to the size and centromere position would help the geneticist work at the identification and also in segmentation, because by defining the chromosome classification in relation to its centromere position, is possible to define their polarity (putting the chromosome "standing"). In the automatic process, it s an excellent filter in the search for a higher correctness rate for chromosomes identification systems, because each type of chromosome always belongs to a particular classification according to the centromere (metacentric, submetacentric or acrocentric). In this dissertation, therefore, sought to develop a series of methods for centromere detection, especially the definition of two algorithms that use the methods developed in this work. As a result it is emphasized that in applying this approach on the image base used from BioImLab (Biomedical Imaging Laboratory, University of Padova, Italy), it achieves about 94.37% of correctness, a higher rate than any work related literature.