Histogram Equalized Thresholding method for analysis of Diabetic Myonecrosis related images

Abstract

Diabetes Mellitus has become a common disorder in all groups of people throughout the world in the current
days, mainly because of sedentary lifestyles and abnormal eating habits. Moreover, it has become a threat as
well as a challenge to the researchers in the scientific society to find a solution to control this problem. Even
though there is no proven cure for this disorder, one can lead a normal life with knowledge and awareness of
issues related to diabetes. The main issue of concern for people with diabetes is its related complications,
especially Myonecrosis. Diabetic myonecrosis is a rare complication of diabetes mellitus that has been poorly
addressed. The disease presents itself in a human as lower extremity acute non-traumatic swelling and pain that
can imitate deep vein thrombosis (DVT). Usually, the medical condition is self-limiting, and patients react well
to therapeutic treatment that supports them. Together with other microvascular complications such as
retinopathy, nephropathy, or neuropathy, it often develops into further complications. These complications are
more common among Type I diabetics but can also arise in patients with Type II diabetes. Current image
processing methodologies hold a significant position in removing specific challenges related to medical
imaging technologies. The research work dealt with in this paper is about the processing of images related to
Diabetic Myonecrosis, and the outcome results are used for analysis of the problem in various conditions of
DM patient with Myonecrosis. Threshold segmentation methods are used to extract the features, and several
attributes have been derived for statistical comparison in this work. The tools used for investigation in this
work are MATLAB Technical computing language for simulation results and MIPAV for deriving statistical
parameters.