Trinion Based WBC Segmentation Using Texture to Detect Acute Lymphoblastic Leukemia
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Date
2021-10
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Addis Ababa University
Abstract
Many diseases are detected based on examination of microscopic images of blood samples.
Changes in the blood condition show the development of diseases in an individual. One type of
disease caused by change of blood condition is Leukemia. Leukemia can cause early death when
it is not treated on time. In Ethiopia, Leukemia accounts to about 35.5% of hematological
admissions. The death rate in Ethiopia due to Leukemia is different with time and region. The
average death rate is increasing from time to time and shows variation between country side and
urban population. Reports from the World Health Organization (WHO) show that death rate due
to Leukemia in Ethiopia has reached 5.56% and ranked 18
th
highest in the world.
Leukemia originates in the bone marrow, a thin material inside the bone. Leukemia is detected
by analyzing white blood cells (WBCs also called Leucocytes), one of the constituents of blood
along with red blood cells (RBC or Erythrocytes), platelets and blood plasma. WBCs have five
different types (Lymphocytes, Myelocytes, Neutrophils, Basophils and Eosinophils) and among
these Lymphocytes and Myelocytes are the ones that could start to change in the bone marrow
and get infected and become Leukemic or infected cells. These Leukemia cells have strange
properties compared to the normal cells in that their growth is abnormal and they survive much
longer than the normal cells. They also interrupt functions of the normal cells. Through time, the
normal cells perish while leukemia cells still survive. Old leukemia cells last for a longer time
and production of new leukemia continue in an abnormal way.
Traditionally, Leukemia detection is carried out manually based on visual examination of
microscopic images of blood samples. This is lengthy and time taking process which depends on
the skills and experiences of the observer which makes the process subjective. In this regard,
computer based automated schemes play their great role and several efforts have been made in
the literature to develop such schemes.
In the current study, a novel mathematical technique for Leukemia detection based on holistic
analysis of color microscopic images of blood samples is proposed. The approach utilizes a
holistic representation of microscopic blood images in the three (Trinion) space and applies
trinion based Fourier transform implemented in the L*a*b color space to extract useful higher
order features to segment normal and infected WBCs and classify them. The technique has been
applied in analyzing microscopic images acquired from standard ALL-IDB database.
Classification of normal and Leukemic WBCs was performed based of Artificial Neural Network
(ANN) which resulted in 95.7% sensitivity, 100% specificity and 97.6% accuracy.
Description
Keywords
Acute Lymphoblastic Leukemia, WBC, Trinion Based