Akbar  Alipour Sifar, Mousa  Shamsi, 
    The Quantitative and Qualitative Evaluation of Simultaneous Segmentation using
Multiplicative Intrinsic Component Optimization in Brain MR Images,
    International Journal of System Signal Control and Engineering Application,
    Volume 12,Issue 6,
    2019,
    Pages 143-147,
    ISSN 1997-5422,
    ijssceapp.2019.143.147,
    (https://makhillpublications.co/view-article.php?doi=ijssceapp.2019.143.147)
    Abstract: Segmentation of brain MR images is a major
issue in medical image processing computations. In brain
MR images, segmentation is caused by an inherent error
which is called intensity in homogeneity. This is due to
the existence of an overlap between different brain tissues
which often causes false classification of tissues. This
paper uses a new proposed method for segmentation and
bias field correction simultaneously which is called
Multiplicative Intrinsic Component Optimization (MICO).
The proposed method, breaks down MR images into two
components, one component characterizes a physical
property of tissue and other inherent bias field that
accounts for the intensity in homogeneity with spatial
features. Then, via. energy minimization in an iterative
process, the above components are optimized and
consequently, segmentation and bias field correction was
carried out, simultaneously. Qualitative assessment of
MICO method was proved in terms of accuracy and
robustness and showed high accuracy of about 90% for
bias field correction and segmentation in three areas of the
brain, especially in the area containing the Cerebrospinal
Fluid (CSF).
    Keywords: Multiplicative intrinsic component optimization algorithm;bias field correction;brain MR image segmentation;magnetic resonance images;characterizes