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Accurate microscopic red blood cell image enhancement and segmentation
conference contribution
posted on 2015-01-01, 00:00 authored by S H Shirazi, A I Umar, N Ul Haq, S Naz, Imran RazzakImran Razzak� Springer International Publishing Switzerland 2015. Erythrocytes (RBC) are the most common type of blood cell. These cells are responsible for the delivery of oxygen to body tissues. The abnormality in erythrocyte cell affects the physical properties of red cell. It may also decrease the life span of red blood cells which may lead to stroke, anemia and other fatal diseases. Until now, Manual techniques are in practiced for diagnosis of blood cell’s diseases. However, this traditional method is tedious, time consuming and subject to sampling error. The accuracy of manual method depends on the expertise of the expert, while the accuracy of automated analyzer depends on the segmentation of objects in microscopic image of blood cell. Despite numerous efforts made for accurate blood cells image segmentation and cell counting in the literature. Still accurate segmentation is difficult due to the complexity of overlapping objects and shapes in microscopic images of blood cells. In this paper we have proposed a novel method for the segmentation of blood cells. We have used wiener filter along with Curvelet transform for image enhancement and noise removal. The snake algorithm and Gram-Schmidt orthogonalization have applied for boundary detection and image segmentation, respectively.
History
Event
Bioinformatics and Biomedical Engineering. International Conference (2017 : Granada, Spain)Volume
9043Series
Lecture Notes in Computer SciencePagination
183 - 192Publisher
SpringerLocation
Granada, SpainPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-04-15End date
2017-04-17ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319164823Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
F Ortuño, I RojasTitle of proceedings
IWBBIO 2015 : Bioinformatics and Biomedical EngineeringUsage metrics
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No categories selectedKeywords
Science & TechnologyLife Sciences & BiomedicineTechnologyBiochemical Research MethodsComputer Science, Information SystemsEngineering, BiomedicalMathematical & Computational BiologyBiochemistry & Molecular BiologyComputer ScienceEngineeringRBCSEMSegmentationWiener filterCurveletCLASSIFICATIONMALARIASMEARS
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