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Journal of Clinical and Experimental Toxicology | Volume: 2
December 03-04, 2018 | Dubai, UAE
International Conference on
6
th
International Conference on
Toxicology, Clinical Toxicology & Pharmacology
Recycling & Waste Management
Joint Event
&
A Python based imaging algorithm to identify necrotic zone for Erythematous Skin Lesions
Satyake Bakshi
Carleton University, Canada
R
uxolitinib which is a JAK-2 inhibitor is an approved drug
used for the treatment of myeloproliferative fibrosis.
It is seen that usage of this drug is not without effects. It
is normally accompanied by exudates on the lips or ulcers.
In severe cases the prolonged use of this drug can lead
to necrolysis of the epidermis. Most of the reactions are
accompanied by a darker necrotic portion. Doctors often
need to find the necrotic portion of a major erythematous
lesion to assess the extent of damage. In the case of multiple
such lesions it is often very time consuming to note the
extent of damage for each lesion. Most detection algorithms
are system and processor heavy when it comes to image
detection programs as they require a much powerful system
i.e. CT images, MRI systems etc. in which the analysis is
to be done. So, a python-based script has been designed
which would run in most systems and can be ported to
other platforms i.e. UNIX or WINDOWS. The purpose of
this algorithm is to specifically identify and automatically
highlight the necrotic portion of such lesions in one pass.
The colour and nature of such lesions is determined by
analysing the individual pixel values around the necrotic
portion. The algorithm is designed based on the open cv
2.4.13 framework of python. Supporting packages of NumPy
and Pandas which are numerical packages typically used for
complex mathematical calculations has been used. NumPy
has been used to generate the contour points concerning
the darker potion of the lesion. The Jupyter compiler has
been used to carry out the analysis. For this algorithm to
work an image of the lesion has been taken and converted to
grayscale and post thresholding by OTSU’s method contours
are approximated depending on the threshold generated.
Contours are the surface profile is roughly visualized by
making use of the canny edge detection algorithm which is
pre-loaded on to the open cv framework. Contours identify
a specific or entire boundary of the object to be separated.
After the contours are generated the best possible set of
points which represent the idle location of the necrotic tissue
are taken and is drawn over the image. The contours can
be either drawn directly or approximated over the image.
From the observation it is seen the later yields near-perfect
identification of the necrotic portions of the lesion. This could
be implemented along with other imaging modalities for
better identification. Future applications of this could include
the use of artificial neural networks for faster detection time.
Speaker Biography
Satyake Bakshi has completed his Bachelor of Technology from Vellore Institute of
Technology, India at the age of 22. He is currently pursuing his master’s degree at Carleton
University, Canada. His area of interest is in rehabilitation engineering and medical image
processing.
e:
satyakeb@gmail.comSatyake Bakshi
, Toxicology 2018 & Recycling 2018, Volume 2
DOI: 10.4066/2630-4570-C1-003