DocumentCode
1316203
Title
Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification
Author
Agam, Gady ; Dinstein, Itshak
Author_Institution
Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Volume
19
Issue
11
fYear
1997
fDate
11/1/1997 12:00:00 AM
Firstpage
1212
Lastpage
1222
Abstract
A common task in cytogenetic tests is the classification of human chromosomes. Successful separation between touching and overlapping chromosomes in a metaphase image is vital for correct classification. Current systems for automatic chromosome classification are mostly interactive and require human intervention for correct separation between touching and overlapping chromosomes. Since chromosomes are nonrigid objects, special separation methods are required to segregate them. Common methods for overlapping chromosomes separation between touching chromosomes tend to fail where ambiguity or incomplete information are involved, and so are unable to segregate overlapping chromosomes. The proposed approach treats the separation problem as an identification problem, and, in this way, manages to segregate overlapping chromosomes. This approach encompasses low-level knowledge about the objects and uses only extracted information, therefore, it is fast and does not depend on the existence of a separating path. The method described in this paper can be adopted for other applications, where separation between touching and overlapping nonrigid objects is required
Keywords
biological techniques; biology computing; cellular biophysics; computational geometry; genetics; image classification; image segmentation; optical microscopy; automatic chromosome classification; cytogenetic tests; geometric separation; identification; image segregation; metaphase image; partially overlapping nonrigid objects; touching objects; Arm; Automatic testing; Biological cells; Computational geometry; Data mining; Humans; Image analysis; Image recognition; Image segmentation; Prototypes;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.632981
Filename
632981
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