Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method - Vivona, Letizia; Cascio, Donato; Taormina, Vincenzo; Raso, Giuseppe (IET computer vision, 2018)
Keywords: ANAs; antinuclear antibodies; autoimmune diseases; automated approach; automatic system; biomedical optical imaging; blood; cellular biophysics; centromere pattern; centromere pattern classification; classification phase; clustering K-means algorithm; complex disorders; diseases; environmental factors; feature extraction; fluorescence; fluorescence intensity; genetic factors; HEp-2 cells; IIF images; image classification; image segmentation; image texture; immune responses; indirect immunofluorescence images classification; medical disorders; medical image processing; MIVIA; prevalent diseases; public database; representative state-of-the-art works; self-antigens; Special Section: Selected Papers from the 19th International Conference on Image Analysis and Processing (ICIAP) 2017; staining patterns; standard screening methods; state-of-the-art techniques; unknown aetiology; unsupervised clustering method; cellular biophysics; unsupervised clustering method; image classification; complex disorders; antinuclear antibodies; self-antigens; centromere pattern; representative state-of-the-art works; MIVIA; automated approach; genetic factors; standard screening methods; immune responses; state-of-the-art techniques; feature extraction; fluorescence; image segmentation; IIF images; medical image processing; autoimmune diseases; ANAs; databases; indirect immunofluorescence images classification; environmental factors; classification phase; diseases; staining patterns; blood; image texture; fluorescence intensity; unknown aetiology; prevalent diseases; medical disorders; HEp-2 cells; public database; automatic system; centromere pattern classification; biomedical optical imaging; clustering K-means algorithm