Now days, blood testing is one of the most
important clinical examinations. The characteristics
(quantity, shape and color) of the white blood cell (WBC) can
give vital information about a patient’s health. But, the
manual inspection is time-consuming and requires adequate
technical knowledge. Therefore, automatic medical diagnosis
systems are necessary to help physicians to diagnose diseases
in a fast and competent way. The main aim of blood cell
segmentation is to extract the cells from complicated
background and to segment every cell into morphological
components such as nucleus, cytoplasm, and some others.
Accuracy of earlier algorithms depends momentously on the
initial contrast of the image. This limitation leads to
capturing of all objects with gray-levels close to that of the
WBCs. To overcome this disadvantage we propose to use the
nucleus minimum segment size as a constraint to eliminate
the non-nucleus objects. The proposed algorithm used,
reduces noise effect and enhances accuracy of segmentation.
All previous methods use different techniques for
segmentation which gives less efficiency compared to
proposed method for nucleus and cytoplasm segmentation. In
this paper, we propose a new method based on gray scale
contrast enhancement and filtering. For removal of false
objects minimum segment size is implemented. Near about
365 blood images will be tested for this technique. Each of
the five normal white blood cell types can be evaluated to
compare separate performance.
Sonali Sonar : North Maharashtra University,
Department of Electronics and Telecommunication Engineering,
J. T. Mahajan College of Engineering, Faizpur,India
Kanchan Bhagat : North Maharashtra University,
Department of Electronics and Telecommunication Engineering,
J. T. Mahajan College of Engineering, Faizpur, India
Blood cell
Dataset
Leucocyte
MATLAB Code
Segmentation
WBC
Segmentation accuracy results for each cell type using
both the proposed algorithm and algorithm proposed by
Madhloom et al. [4]. It shows that the superiority of
proposed algorithm for each cell type and for the overall
performance of 79.7% compared to 55.9%.
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