Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosing autoimmune diseases can be particularly difficult because these disorders can affect any organ or tissue in the body, and produce highly diverse clinical manifestations, depending on the site of autoimmune attack. Moreover, disease symptoms are often not apparent until the disease has reached a relatively advanced stage. These observations suggested us to develop an automated method to support the diagnosis. Developing an automated procedure for diagnosis of autoimmune diseases, generally authors focus their attention on cells detection, fluorescence intensity determination or pattern classification. Since different patterns correspond to different diseases, it is really important to be able to distinguish among different pattern, so we chosen to deal with the pattern recognition. Particularly we propose here a method to automatically classify the Centromere pattern based on the grouping of centromeres present on the cells through a clustering algorithm.
Vivona, L. (2014). A physical-computational modelling for analysis of Centromere patterns in IIF images.
|Titolo:||A physical-computational modelling for analysis of Centromere patterns in IIF images|
|Data di pubblicazione:||24-feb-2014|
|Citazione:||Vivona, L. (2014). A physical-computational modelling for analysis of Centromere patterns in IIF images.|
|Appare nelle tipologie:||4.2 Tesi di dottorato|
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