File(s) under permanent embargo
Efficient leukocyte segmentation and recognition in peripheral blood image
journal contribution
posted on 2016-01-01, 00:00 authored by S H Shirazi, A I Umar, S Naz, Imran RazzakImran RazzakBACKGROUND: Blood cell count, also known as differential count of various types of blood cells, provides valuable information in order to assess variety of diseases like AIDS, leukemia and blood cancer. Manual techniques are still used in diseases diagnosis that is very lingering and tedious process. However, machine based automatic analysis of leukocyte is a powerful tool that could reduce the human errors, improve the accuracy, and minimize the required time for blood cell analysis. However, leukocyte segmentation is a challenging process due to the complexity of the blood cell image; therefore, this task remains unresolved issue in the blood cell segmentation. OBJECTIVE: The aim of this work is to develop an efficient leukocyte cell segmentation and classification system. METHODS: This paper presents an efficient strategy to segment cell images. This has been achieved by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges. We have also used combination of entropy filter, thresholding and mathematical morphology for obtaining image segmentation and boundary detection, whereas we have used back-propagation neural network for leukocyte classification into its sub classes. RESULTS: As a result, the generated segmentation results are fruitful in a sense that we have overcome the problem of overlapping cells. We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15% accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil respectively.
History
Journal
Technology and health careVolume
24Issue
3Pagination
335 - 347Publisher
IOS PressLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0928-7329Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2016, IOS Press and the authorsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC