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Previous issue date: 2014-12-26 / A detec??o autom?tica de componentes sangu?neos em imagens microsc?picas ? um
importante t?pico da ?rea hematol?gica. A segmenta??o permite que os componentes
sangu?neos sejam agrupados em ?reas comuns e a classifica??o diferencial dos leuc?citos
possibilita que os mesmos sejam analisados separadamente. Com a segmenta??o autom?tica e
classifica??o diferencial, contribui-se no processo de an?lise dos componentes sangu?neos,
fornecendo ferramentas que propiciem a diminui??o do trabalho manual e o aumento da sua
precis?o e efici?ncia. Utilizando t?cnicas de processamento digital de imagens associadas a
uma abordagem fuzzy gen?rica e autom?tica, este trabalho apresenta dois Sistemas de
Infer?ncia Fuzzy, definidos como I e II, para a segmenta??o autom?tica de componentes
sangu?neos e classifica??o diferencial de leuc?citos, respectivamente, em imagens
microsc?picas de esfrega?os. Utilizando o Sistema de Infer?ncia Fuzzy I, a t?cnica
desenvolvida realiza a segmenta??o da imagem em quatro regi?es: n?cleo e citoplasma
leucocit?rios, eritr?citos e ?rea de plasma e utilizando o Sistema de Infer?ncia Fuzzy II e os
leuc?citos segmentados (n?cleo e citoplasma leucocit?rios), os classifica diferencialmente em
cinco tipos: bas?filos, eosin?filos, linf?citos, mon?citos e neutr?filos. Foram utilizadas para
testes 530 imagens contendo amostras microsc?picas de esfrega?os sangu?neos corados com
m?todos variados. As imagens foram processadas e seus ?ndices de Acur?cia e Gold
Standards foram calculados e comparados com os resultados manuais e com outros resultados
encontrados na literatura para os mesmos problemas. Quanto ? segmenta??o, a t?cnica
desenvolvida demonstrou percentuais de acur?cia de 97,31% para leuc?citos, 95,39% para
eritr?citos e 95,06% para plasma sangu?neo. Quanto ? classifica??o diferencial, os percentuais
variaram entre 92,98% e 98,39% para os diferentes tipos leucocit?rios. Al?m de promover a
segmenta??o autom?tica e classifica??o diferencial, a t?cnica desenvolvida contribui ainda
com defini??o de novos descritores e a constru??o de um banco de imagens utilizando
diversos processos de colora??o hematol?gicos / Automatic detection of blood components is an important topic in the field of
hematology. The segmentation is an important stage because it allows components to be
grouped into common areas and processed separately and leukocyte differential classification
enables them to be analyzed separately. With the auto-segmentation and differential
classification, this work is contributing to the analysis process of blood components by
providing tools that reduce the manual labor and increasing its accuracy and efficiency.
Using techniques of digital image processing associated with a generic and automatic fuzzy
approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation
of blood components and leukocyte differential classification, respectively, in
microscopic images smears. Using the Fuzzy Inference System I, the proposed technique
performs the segmentation of the image in four regions: the leukocyte?s nucleus and
cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the
segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types:
basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530
images containing microscopic samples of blood smears with different methods. The images
were processed and its accuracy indices and Gold Standards were calculated and compared
with the manual results and other results found at literature for the same problems.
Regarding segmentation, a technique developed showed percentages of accuracy of 97.31%
for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential
classification, the percentage varied between 92.98% and 98.39% for the different leukocyte
types. In addition to promoting auto-segmentation and differential classification, the
proposed technique also contributes to the definition of new descriptors and the construction
of an image database using various processes hematological staining
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/19642 |
| Date | 26 December 2014 |
| Creators | Vale, Alessandra Mendes Pacheco Guerra |
| Contributors | 01207607703, http://lattes.cnpq.br/8556144121380013, D?ria Neto, Adri?o Duarte, 10749896434, http://lattes.cnpq.br/1987295209521433, Martins, Allan De Medeiros, 01979076448, http://lattes.cnpq.br/4402694969508077, Leite, Cicilia Raquel Maia, 03777857416, http://lattes.cnpq.br/9378258073324535, Carvalho, Marco Antonio Garcia de, 59564245400, http://lattes.cnpq.br/6366443994619479, Guerreiro, Ana Maria Guimar?es |
| Publisher | Universidade Federal do Rio Grande do Norte, PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| 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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