Unsupervised detection of fibrosis in microscopy images using fractals and fuzzy c-means clustering
Fecha
2012Materia
Resumen
The advances in improved fluorescent probes and better cameras in collaboration with the advent of computers in imaging and image analysis, assist the task of diagnosis in many fields of biologic and medical research. In this paper, we introduce a computer-assisted image characterization tool based on a Fuzzy clustering method for the quantification of degree of Idiopathic Pulmonary Fibrosis (IPF) in medical images. The implementation of this algorithmic strategy is very promising concerning the issue of the automated assessment of microscopic images of lung fibrotic regions. © 2012 IFIP International Federation for Information Processing.
Colecciones
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Load curves partitioning with the application of soft clustering algorithms
(2019)Load profiling refers to a procedure which leads to the formulation of daily load curve and consumer categories regarding the similarity of their curves shapes. This procedure incorporates a set of pattern recognition ... -
Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting
(2022)The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting ... -
Management zones delineation using fuzzy clustering techniques in vines
(2011)This paper presents the results of applying precision agriculture in a 1 ha commercial vineyard in Greece in 2009 and 2010. A field elevation map was produced using RTK-GPS. Soil electrical conductivity was measured using ...