Authors:
Sarah Ghandour
;
Eric Gonneau
and
Guy Flouzat
Affiliation:
LERISM, Toulouse University, France
Keyword(s):
Segmentation, Watershed Algorithm, Region Adjacency Graph, Mathematical Morphology, Generalized Likelihood Ratio, Clustering, Hypercube Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Informatics in Control, Automation and Robotics
;
Mathematical Morphology
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
Abstract:
In this paper, a new color segmentation scheme of microscopic color images is proposed. The approach combines a region growing method and a clustering method. Each channel plane of the color images is represented by a set of regions using a watershed algorithm. Those regions are represented and modeled by a Region Adjacency Graph (RAG). A novel method is introduced to simplify the RAG by merging candidate regions until the violation of a stopping aggregation criterion determined using a statistical method which combines the generalized likelihood ratio (GLR) and the Bayesian information criterion (BIC). From the resulting segmented and simplified images, the RGB image is computed. Structural features as cells area, shape indicator and cells color are extracted using the simplified graph and then stored in a database in order to elaborate meaningful queries. A regularization step based on the use of an automatic classification will take place. Results show that our method that does no
t involve any a priori knowledge is suitable for several types of cytology images.
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