3. INTRODUCTION
Karyotype analysis is a widespread procedure in cytogenetics to
assess the possible presence of genetics
defects. The procedure is lengthy and repetitive, so that an
automatic analysis would greatly help the
cytogeneticist routine work.
Hence, an automatic procedure to obtain the separated
chromosomes, which are then ready for a subsequent
classification step, would have significant implication.
Using karyotype we can diagnose Aneuploidy i.e. an abnormal
number of chromosomes in the cells of an individual.
major example is Down's syndrome. Besides aneuploidies there
are other genetic diseases, other chromosomal abnormalities and
also the genetic mutations.
4. METHODOLOGY
• The first step for classification is the extraction of chromosome
features.
• Chromosomes features include length,area,density profile and
its perimeter.
• Polarization: Chromosomes in the acquired image are
randomly rotated. A polarization procedure is needed both to
comply with the orientation standard adopted by cytogenetics.
• Feature Pre-processing: The dataset used in this study contains
images acquired with different zoom and illumination
conditions, hence data has to be normalized first.
• A prior trained Artificial neural network is used as classifier.
• The Reassigning Algorithm: The human karyotype contains 23
chromosomes hence classification is constrained on this fact
and classified.
5. HURDLES AND COMPLICATIONS
• The appearance of chromosomes depends on the stage of the
cell division cycle and are visible as distinctive entity only in
prometaphase stage.
• Dealing with touches and overlaps.
• Nonsharp margins of the chromosomes.
• Presence of staining debris due to restricted image resolution.
• Uneven illumination of the field of view by the UV lamp.
• Low resolution images.
6. FEATURE EXTRACTION
• As of now we are able to extract chromosomes features using
MATLAB and Mathametica.
• Using individual chromosomes dataset we apply certain image
enhancement technique and transforms to compute the
required data.
• The classification features starts from an estimation of the
chromosome medial axis, derived by a skeletonization
procedure. Unfortunately, in many automatically segmented
images this method provides a skeleton with a lot of spurious
branches.
• Hence spurious branches have to be removed using spur
reduction technique.
7. FEATURE EXTRACTION(cont.)
• After spur reduction and thinning procedure we are left up with
medial axis which is of foremost importance,
• Using medial axis we can compute length of individual
chromosomes.
• Density profile of each chromosomes is obtained by drawing
perpendicular over medial axis.
• The area and perimeter of chromosomes is evaluated after
suited transform over the binary image.
8. TRANSFORMS AND ENHANCEMENT
USED
• fspecial(‘gaussian ', hsize, sigma) is a MATLAB code that
returns a rotationally symmetric Gaussian lowpass filter of
size hsize with standard deviation sigma (positive). hsize can
be a vector specifying the number of rows and columns in h, or
it can be a scalar, in which case h is a square matrix. The
default value for hsize is [3 3]; the default value for sigma is
0.5.
• imfilter(i.e.'replicate') is a MATLAB code such that Input
array values outside the bounds of the array are computed by
mirror-reflecting the array across the array border.
• graythresh: MATLAB code to find out Global image threshold
using Otsu's method.
• bwmorph: MATLAB code for morphological operations on
binary images.
9. TRANSFORMS AND ENHANCEMENT
USED(cont.)
• skel: MATLAB code such that with n = Inf, removes pixels on
the boundaries of objects but does not allow objects to break
apart. The pixels remaining make up the image skeleton. This
option preserves the Euler number.
• spur: MATLAB code that removes spur elements from the
image matrix.
• bwarea(BW) is a MATLAB code that estimates the area of the
objects in binary image BW. total is a scalar whose value
corresponds roughly to the total number of on pixels in the
image, but might not be exactly the same because different
patterns of pixels are weighted differently.
• ComponentMeasurements[ Morphological Components[image]
,"Elongation"] is a Mathametica code to find out the length of
individual component in an image.
10. FLOW PROCESS
Gaussian lowpass filtering & mirror-
reflecting the array across the array border
Skeleton transform
Spur reduction
graythresh
12. CONCLUTION:
Using software like MATLAB and Mathametica we have
successfully performed image enhancement and feature
extraction of chromosomes like it’s medial axis construction
and length measurement using medial axis, area and perimeter
measurement have also been performed and the corresponding
data obtained from individual images are normalized using
maximum limit.
Now we are looking forward to next advanced stages of
finding density profile, complete data set production and their
normalization. After which we can train a ANN classifier to
recognize each individual chromosome and classify them
accordingly to a karyotype image.
On successful completion of project we can take forward the
legacy of Alfredo Ruggeri which he had pioneered in the field
of Bio-medical Imaging.
