Paper Info
Title
Detecting leukaemia (AML) blood cells using cellular automata and heuristic search
Abstract
This paper presents a method for the identification of leukaemia cells within images of blood smear microscope slides, which is currently a time consuming manual process. The work presented is the first stage of a procedure aimed at classifying the sub-types of Acute Myeloid Leukaemia. This paper utilises the techniques of Otsu, Cellular Automata and heuristic search and highlights a comparison between random and seeded searches. We present a novel Cellular Automata based technique that helps to remove noise from the images and additionally locates good starting points for candidate white blood cells. Our results are based on real world image data from a Haematology Department, and our analysis shows promising initial results.
Year
DOI
Venue
2010
10.1007/978-3-642-13062-5_7
IDA
Keywords
Field
DocType
real world image data,leukaemia cell,heuristic search,haematology department,cellular automaton,initial result,acute myeloid leukaemia,candidate white blood cell,detecting leukaemia,cellular automata,novel cellular automata,blood smear microscope slide,simulated annealing,hill climbing
Hill climbing,Cellular automaton,Heuristic,Pattern recognition,Computer science,Haematology department,Artificial intelligence,Machine learning
Conference
Volume
ISSN
ISBN
6065
0302-9743
3-642-13061-5
Citations 
PageRank 
References 
3
0.45
8
Authors
3
Name
Order
Citations
PageRank
Waidah Ismail182.95
Rosline Hassan2112.61
Stephen Swift342731.32