Review on Image Segmentation Techniques to Detect Outliers in Blood Samples
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : P.Poornima, S.Saranya
|DOI : 10.14445/22315381/IJETT-V53P212|
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
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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Leukemia, Image segmentation, Anomaly detection, Clustering algorithms