Journal
IEEE SYSTEMS JOURNAL
Volume 8, Issue 3, Pages 995-1004Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2014.2308452
Keywords
Acute myelogenous leukemia (AML); classification; feature extraction; segmentation
Categories
Funding
- U.S. National Science Foundation [HRD-0932339]
Ask authors/readers for more resources
Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults. The average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming, and its accuracy depends on the operator's ability. In this paper, a simple technique that automatically detects and segments AML in blood smears is presented. The proposed method differs from others in: 1) the simplicity of the developed approach; 2) classification of complete blood smear images as opposed to subimages; and 3) use of these algorithms to segment and detect nucleated cells. Computer simulation involved the following tests: comparing the impact of Hausdorff dimension on the system before and after the influence of local binary pattern, comparing the performance of the proposed algorithms on subimages and whole images, and comparing the results of some of the existing systems with the proposed system. Eighty microscopic blood images were tested, and the proposed framework managed to obtain 98% accuracy for the localization of the lymphoblast cells and to separate it from the subimages and complete images.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available