Review on Image Segmentation Techniques to Detect Outliers in Blood Samples
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International Journal of Engineering Trends and Technology (IJETT) | 
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| © 2017 by IJETT Journal | ||
| Volume-53 Number-2 |
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| Year of Publication : 2017 | ||
| Authors : P.Poornima, S.Saranya |
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| DOI : 10.14445/22315381/IJETT-V53P212 |
Citation
P.Poornima, S.Saranya "Review on Image Segmentation Techniques to Detect Outliers in Blood Samples", International Journal of Engineering Trends and Technology (IJETT), V53(2),64-73 November 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
A person’s health is determined by
complete blood count which consisting of white blood
cells, the red blood cells and platelets. Leukemia
occurs when a lot of abnormal white blood cells are
produced by bone marrow thereby leading to cancer.
In laboratory, blood cell counting often produces
inaccurate and unreliable results since usage of
hemocytometer or microscope is laborious and time
consuming task. Images are used as they are cheap
and do not require expensive testing and lab
equipment. Image processing is a strategy to extract
useful characteristics from original image through
different stages. The primary stages of noise or outlier
removal are image acquisition, preprocessing, image
enhancement, image segmentation, feature extraction.
Image Segmentation is a process of identifying blood
cell types regardless of their irregular shapes, sizes,
and orientation. This survey reviews on the different
image segmentation strategies adopted by researchers
in detecting leukemia from blood microscopic image
to boost the clustering performance thereby
eliminating noise in cell image.
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Keywords
Leukemia, Image segmentation,
Anomaly detection, Clustering algorithms