Detection of abnormalities in Fetus using Medical Image Processing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1667
Detection of abnormalities in Fetus using Medical Image Processing
Brainis Vinslet Michael1& Mrs. Dr.T.S.Sheela Shiny, M.E.,Ph.D2
1 FINAL YEAR PG STUDENT, St. Xavier’s Catholic College of Engineering
2 ASSOCIATE PROFESSOR St. Xavier’s Catholic College of Engineering
---------------------------------------------------------------------***-------------------------------------------------------------------
Abstract — Fetal MRI volumetry is a valuable method yet
it is restricted by a reliance upon movement free sweeps,
monotonous manual division, and spatial error because of
thick slice checks. A picture handling pipeline that locations
these impediments was created and tested. The chief
successions gained in fetal MRI clinical practice are different
symmetrical single- shot quick twist reverberation checks.
Cutting edge picture handling methods were utilized for
between cut movement adjustment also, super-goal
recreation of high-goal volumetric pictures from these
outputs. The recreated volume pictures were processed with
force non-consistency amendment furthermore, the fetal
mind removed by utilizing directed robotized segmentation.
Reconstruction, division and volumetry of the fetal
cerebrums for a partner of 25 clinically procured fetal MRI
filters was finished. Execution measurements for volume
reproduction, division and not entirely set in stone by
contrasting with manual drawings in five arbitrarily picked
cases. At long last, examination of the fetal mind and
parenchymal volumes was performed in view of the
gestational age of the babies. The picture handling pipeline
created in this study empowers volume delivering and
precise fetal cerebrum volumetry by tending to the
impediments of current volumetry methods, which
remember reliance for movement free filters, manual
division, and erroneous thick-cut insertion.
Key Words : Fetal MRI · Brain volumetry ·Image
reconstruction · Segmentation
I INTRODUCTION
The progress from woman to motherhood has been given
significance from days of yore. The parenthood is a
wonderful stage in ladies' life. During pregnancy, care to
the mother and hatchling are fundamental. In this period a
few pregnancy related issues are happen to the mother.
Mother might experience a few medical issues, such as
morning disorder, clogging, gestational diabetes,
queasiness and retching, vision issues and so on. Similarly
significant is the wellbeing of the baby and its turn of
events. In this way the baby is to be checked persistently
till its introduction to the world. The existence of a baby
inside the mother's belly is separated in to three periods.
In-vivo fetal imaging procedures, for example,
ultrasonography and attractive reverberation imaging
(MRI) are of central significance in assessing and deciding
the improvement of the hatchling. Biometric regulating
values for gestational age (GA) have been laid out in-vitro
by assessment of fixed minds, and furthermore through
estimating huge partners of sameage babies utilizing
ultrasonography. Fetal MRI is a somewhat more up to date
methodology, used in the assessment of high-risk
pregnancies where there is worry for peculiarities in light
of distinguished chances, like high level maternal age,
family ancestry, maternal openings, and irregularities
identified by routine pre-birth ultrasound. X-ray has been
helpful in assessing anomalies of fetal designs which are
hard to completely survey by pre-birth sonography alone,
like those including the fetal mind, chest, and aviation
route. X-ray doesn't include ionizing radiation, affects
either the pregnant patient or the hatchling, and enjoys
numerous upper hands over sonography, including that it
is less restricted by diminished amniotic liquid, maternal
corpulence, or troublesome fetal position. Fetal cerebral
biometry principally includes length estimates which are
evaluated by obstetric imagers on two-layered sees. The
most generally acquired proportions of fetal mind size
incorporate cerebral and skull biparietal width (BPD),
occipito front facing measurement (OFD), and head
periphery (HC). Cross over cerebellar width, vermian
profundity and level, corpus callosal length, and the size
and distance between circles are likewise every now and
again estimated . Regularizing biometric values have been
gotten through evaluations of extremely huge associates of
fetal sonographic pictures. Reference biometric values for
the fetal cerebrum have been as of late acquired in light of
huge arrangements of MRI pictures of 589 hatchlings [6].
The high level imaging advancements past 2D sonography,
for example 3D ultrasound and MRI, lead to more
illustrative biometric measures in view of the volume and
state of fetal organs and designs. Fetal mind volumetry is
viewed as critical in the assessment of fetal turn of events .
A typical pregnancy period as a rule ranges between 38 to

42 gestational weeks (GW). The pregnancy is partitioned
into three-month time frames called trimesters. First
Trimester (0 to 13 GW): The initial three-month trimester
is by and large determined as beginning the initial day of
the last time frame and goes through the thirteenth seven
day stretch of the pregnancy. During this trimester, the
embryo inserts into the belly of the lady. Second Trimester
(14 to 27 GW): This period begins in the fourteenth seven
day stretch of the pregnancy and goes through the 27th
week. During the second trimester, the hatchling starts to
quickly develop. By about the twentieth seven day stretch
of the incubation time frame, most ladies will start to feel
development of the embryo. Third Trimester (28 to
conveyance): This period begins in the 28th seven day
stretch of the development time frame and goes through
the introduction of the youngster. During this period, the
hatchling expansions in size and stretches the uterus and
mid-region of the lady. In the initial segment of the third
trimester, the embryo will be at its generally dynamic and
development will be hard to miss. As the kid occupies the
accessible space, development will turn out to be less yet
more grounded and the lady will turn out to be more
awkward. The fetal mind is a unique construction,
radiologists genuinely must get to know the ordinary
appearance of the fetal cerebrum at various GW to be
better ready to recognize and portray fetal cerebrum
irregularities . During the most recent twenty years, fetal
mental health has turned into a significant subject in
neuroscience because of current painless and
computational procedures. The consequences of such
methods permit quantitative depiction of the fetal mind
structure, which incorporates improvement of individual
nerve cells and whole organizations inside unambiguous
cerebrum regions . A few surveys on fetal mental health
showed up during the past. In-vivo fetal imaging is a
significant method in envisioning and assessing the
advancement of fetal designs, particularly for fetal
cerebrum. While it tends to be exceptionally pulverizing to
figure out that the embryo has an irregularity, pre-birth
determination can permit time to settle on informed
choices. Research investigations of fetal MRI of ordinary
cerebrums at various GW permit laying out of
standardizing measures that can be utilized in early
distinguishing proof of formative mind irregularities.
Research examinations keep on distinguishing a rising
number of explicit quality imperfections for mind
distortions.The effective conveyance of hereditary material
to the creating fetal mind addresses an advancement in
concentrating on mental health and treating of various
deadly cerebrum problems. Fetal MRI, particularly with
utilizing new arrangements, can possibly characterize
underlying, physiological, and metabolic parts of cerebrum
contortions that can uphold research on quality treatment
and lead to the treatment or avoidance of these sicknesses.
II RELATED WORK
Many articles have tended to fetal cerebrum volumetry
utilizing both MRI and 3D ultrasound [7-16], however little
advancement has been made in the calculation techniques
utilized. Most strategies depend on the (changed) Cavalieri
technique for fair-minded volume assessment. Based on
the Cavalieri guideline, an unprejudiced assessment of the
mean absolute volume of an arbitrary shaped item can be
acquired from the areas on systematic uniform-arbitrary
cuts (areas). A few strategies exist for assessment of the
areas of cuts, yet the most effective strategy is viewed as
point-counting. Early MRI-put together volumetry
techniques [10,11] depended with respect to cuts as thick
as 10mm which incite too extraordinary an estimation
mistake for tiny fetal mind sizes (for example 50th
percentile typical cerebral BPD is 80mmfor a
35weekGAfetus). Comparable volumetry strategies in light
of ultrasonography [7,13] likewise depend on manual
following and estimation of the area of 8-10 sequential
coronal cuts. In these examinations, the fetal cerebrum
volume is assessed via consequently adding vertical round
and hollow in the middle of between the cuts which looks
like a direct insertion between thick-cut areas. In another
examination in light of ultrasound [16], a circular shape
was expected for fetal head and the assessed volumes
were contrasted with distributed posthumous information
on fetal brain weight matched for GA also, the particular
gravity of the brain. An assessment factor was
consequently got for mind volumetry. In different
examinations [8,12], mind volumetry was performed by
choosing the best unique single-shot quick twist
reverberation (SSFSE) X-ray examines got during a solitary
filtering meeting. Volume Calculation was acted in these
examinations through devoted programming, however
once more, the computation was in view of MRI sweeps of
4-5mm cut thickness and utilization of manual tracing.
Volumetry was performed for ten babies in [12] and for
fifty embryos of 17-37 weeks GA in [8]. The length of the
time expected to perform manual drawings has been
distinguished as a significant constraint [8].Three basic
impediments exist in fetal cerebrum MRI volumetry
strategies; first, fetal and maternal motion may
fundamentally debase the MRI examines and their
reasonableness for volumetry estimation. Albeit top notch
cuts are regularly gotten by SSFSE because of short cut
obtaining time, serious inter slice movement actuated
contrasts often show up in out of- plane perspectives.
Accordingly, the gained SSFSE outputs can seldom be

resampled and meant satisfactory volumetric pictures.
Second, thick cuts (ordinarily 3-5mm) are gained in fetal
MRI to keep a high sign to-commotion proportion. This
constraint brings about significant estimation mistakes in
resampling and volumetry, explicitly while looking at little
measured fetal designs. Third, physically following and
dividing fetal cerebrum structures is dreary and tedious,
altogether lessening interest in the standard utilization of
fetal cerebrum volumetry. To address the impediments of
fetal mind MRI volumetry methods, we have fostered a
picture handling pipeline in light of between cut
movement revision, super goal volume remaking, force
non-consistency remedy and directed division. Our
technique depends on an as of late evolved strategy for
super-goal volume remaking from cut acquisitions [17,18].
The created strategy gives high-goal volumetric pictures
which address reasonable anatomic limits that are not
clear in unique SSFSE filters. Accordingly, the remade
volumetric pictures permitted us to create directed picture
division procedures to work on the exactness and
simplicity of acquiring exact fetal cerebrum volumetry.
III OVERVIEW OF OUR SOLUTION
Fetal cerebrum MRI includes various single-shot quick
twist reverberation (SSFSE) picture acquisitions in the
symmetrical perspectives on the hatchling which typically
give high-goal and great cuts within the sight of
discontinuous fetal and maternal movement. In any case,
because of thick-cut acquisitions and between cut
movement, these pictures don't mirror the anatomic
subtleties and cognizant tissue limits in the three layered
sees hence can't be actually utilized for tissue division and
exact mind volume attempt. A portrayal of our picture
handling pipeline in light of movement revision, super-goal
volume recreation, and managed mind tissue division
which permits exact fetal cerebrum volumetry
Fig-1 System Architecture
There have been a couple of ongoing examinations tending
to high resolution volumetric picture recreation from
numerous multislice MRI filters [22-24]. These methods
depend on between cut movement adjustment and
dispersed information insertion (SDI) for volumetric
picture remaking. There are specialized contrasts between
these strategies in light of movement remedy draws near
and the SDI strategies utilized [22-24]. By the by, these
methods don't give an fitting numerical plan to legitimize
that the recreated volume is a base mistake portrayal of
the fundamental life systems given the gained thick-cut
checks. In our recent work on super-goal volume
recreation from cut acquisitions [17,18], we fostered a
numerical structure for mistake minimization in light of a
cut
Acquisition model. This approach thinks about an actual
model for the SSFSE checks as piles of thick cuts situated in
the three-layered space. The cut securing model is a
forward model, which depicts how the procured cuts are
gotten from the imaged object during the MRI cut
procurement technique.
Figure 1 portrays a straightforward chart of the cut
procurement model. This model integrates movement of
the imaged object in three-layered space and the MRI cut
securing process which includes cut choice, signal
obtaining, quantization also, examining. The result is a
computerized portrayal of a thick cut of the imaged object.
In the super-goal volume remaking plan, a discrete form of
this model is viewed as in which the information is an ideal
high-goal volumetric portrayal of the imaged object. The
info and result pictures are addressed by vectors of the
picture voxels in the lexicographical request. All the
picture administrators between the info furthermore, the
result can be demonstrated as grid tasks, consequently a
straight model portrayal is accomplished. The super-goal
volume remaking is accordingly characterized as the
converse issue of tracking down the ideal high-goal
portrayal of the imaged object given an adequate number
of obtained cut examines. The definition and arrangement
of this straight backwards issue is completely talked about
in [17] and [18].
IV. PROPOSED ALGORITHMS
A. Volume reconstruction
On super-goal volume recreation from cut
acquisitions A numerical system for mistake minimization
in light of a cut procurement model. This approach
considers an actual model for the SSFSE checks as piles of

thick cuts situated in the three-layered space. The cut
securing model is a forward model, which depicts how the
gained cuts are gotten from the imaged object during the
MRI cut obtaining technique. portrays a straightforward
graph of the cut obtaining model. This model consolidates
movement of the imaged object in three-layered space and
the MRI cut obtaining process which includes cut
determination, signal procurement, quantization and
examining. The result is a computerized portrayal of a
thick cut of the imaged object. In the super-goal volume
reproduction plan, a discrete variant of this model is
viewed as in which the info is an ideal high-goal volumetric
portrayal of the imaged object. The information and result
pictures are addressed by vectors of the picture voxels in
the lexicographical request. All the picture administrators
between the info and the result can be displayed as lattice
tasks, in this manner a direct model portrayal is
accomplished. The super-goal volume recreation is
consequently characterized as the converse issue of
finding the ideal high-goal portrayal of the imaged object
given an adequate number of gained cut filters.
B. Slice motion estimation and scattered
data interpolation
Cut movement assessment in fetal MRI is very
troublesome. This procedure is like the strategies utilized
execution contrasts in view of the expense capabilities
utilized and the improvement methods. In cut to-volume
enrollment, the movement boundaries of each cut are
figured by enlisting that cut to a reference volume. For
each cut, a 6 level of-opportunity three-layered inflexible
change is figured by limiting the mean square power
contrasts (MSD) between that cut and the reference
volume. By the by, a high-goal reference volume isn't
accessible for this reason. Accordingly, a guess of the
remade volumetric picture is utilized as the reference for
enlistment. This guess is at first registered as the normal of
the resampled SSFSE checks and is worked on through
cycles of cut movement assessment and super-goal volume
remaking. After cut to-volume enrollment, the cut picture
voxels won't be on an ordinary matrix and are considered
as dispersed data of interest in the three-layered space.
Dispersed information introduction (SDI) is expected to
get a first assessment of the remade volume picture from
this information. The SDI approach used here depends on
nearby force infusion through Gaussian bits. The
dissipated information focuses got from movement
amended cuts are planned to their closest neighbor matrix
focuses in the reproduced picture space, and their force
values are infused into the local focuses through Gaussian
portion loads. This underlying appraisal of the recreated
volume is refined through greatest probability super-goal
volume remaking created to this whole iterative
reproduction calculation as ML-SVR here, for most
extreme probability super-goal volume reproduction.
C.Intensity non-uniformity correction
The correction of intensity non-uniformity is crucial prior
to automated image segmentation. The intensity non-
uniformity artifacts can be severe in fetal images due to
weak signal, fetal positioning, cardiac coil placement, and
magnetic field bias. We use the fully automatic entropy
minimization approach of Mangin for estimating a smooth
multiplicative field for intensity non-uniformity correction
. The idea is to find the parameters of a regular grid of
points with cubic Spline interpolation as a smooth model
of the correction field (Fc).
Fopt c = argfc min Fc [KH H (FcX) + KR R (Fc) + KM M
(FcX)]
where X is the reconstructed volumetric image, H (FcX) is
the entropy of the corrected image, R (Fc) is a
regularization function which measures the field
smoothness, and M (FcX) is a quadratic measure of the
distance between the reconstructed volume and the
corrected volume. The weighting constants are set as K H =
1, KR = 0.01, and K M = 0.5, and the grid point spacing was
set between 32 and 64 mm in our experiments depending
on the size of the image and the intensity distortions.
D. Segmentation
Segmentation of fetal MRI images is very challenging due
to low resolution, low signal contrast, the differences in
developing brain tissue types, and motion and intensity no
uniformity artifacts. Therefore, most of the studies on fetal
brain volume try rely on manual tracings for segmentation
of original thick-slice SSFSE scans. The super-resolution
reconstruction of high-resolution volumetric images
discussed earlier allows us to use automated segmentation
algorithms more effectively. Since our goal is brain volume
try, we focus on the segmentation of brain tissue and
cerebrospinal fluid (CSF). Bone structures such as skull are
hypo intense and the CSF is hyper intense on T2-weighted
fetal MRI images, so if the motion artifacts and intensity
non-uniformity are effectively corrected by the algorithms
discussed in the previous sections, automated
segmentation is feasible. Nevertheless, due to the
complexity and high variability of the developing brain
tissue and the differences in contrast due to clinical
imaging parameters and the positioning of the fetal brain,

it is difficult to establish a set of parameters for automated
segmentation of fetal brains for all gestational ages.
V. RESULTS
Fetal MRI information were gotten from clinical MRI of
patients with analyzed or associated cases with fetal
inconsistencies later indicative ultrasonography. Clinical
fetal MRI was performed utilizing a 1.5-T TwinSpeed Signa
framework (GE medical care) what's more, a 8-channel
staged cluster cardiovascular loop, without maternal
sedation or breath-hold, with the mother in left decubitus
position to limit caval pressure. The convention involved
various acquisitions in the fetal sagittal, pivotal
furthermore, coronal sees utilizing half-Fourier obtaining
single-shot quick twist reverberation (SSFSE)magnetic
reverberation imaging with TR differing somewhere in the
range of 1,000 and 4,500 ms; TE shifting between 80 also,
100 ms; variable field of view in light of the maternal and
fetal body size (somewhere in the range of 24 and 40 cm);
variable grid size somewhere in the range of 160 and 512;
and cut thickness of 3 or 4mm. Clinical fetal MRI at our
foundation includes SSFSE examines with different TR and
TE values which bring about various picture contrasts. For
the tests in this review we just use sets of SSFSE examines
with a similar difference.
FIG-2 Intensity correction image
The reconstructed volumetric fetal MRI images were
processed by intensity non-uniformity correction and
segmentation algorithms discussed in this article. Figure 3
shows different stages of segmentation to achieve brain
volumetry. These stages involve intracranial segmentation,
tissue classification using multiple intensity-based
thresholding, brain parenchyma segmentation based on
the tissue classifications using connected components
algorithm, morphological filtering, and level set
segmentation, and finally brain volume segmentation by
adding parenchymal and ventricular volumes processed
with morphological filtering (closing operation) and
connected components algorithm. The brain volume is
segmented with this procedure because in practice the
brain volume is computed as the volume enclosed by brain
parenchyma including the ventricles.
FIG-3 Anisotropic diffusion gaussian filtering
FIG-4 The performance of data enhancement
V CONCLUSION
The catch scope of our cut to-volume enrollment
calculation relies upon a few boundaries including the
nature of the assessed volumetric picture utilized as
enlistment focus, as well as the picture highlights which
thus relies upon the GA of the embryo and the picture
contrast. Practically speaking we don't anticipate that the
calculation should catch and address between cut
movement of 8 mm or more. Assuming movement
antiques stay in the recreated picture or the force non-
consistency can't be reestablished, the division. calculation
might come up short and volumetry may not be precise.
Nonetheless, it is normal that with better and quicker
imaging methods fetal pictures of a superior quality can be
gained which will prompt expansions in the achievement
pace of our calculation. At last, the created strategies in
this study can be applied to other fetal organs. Here we
report a starter reproduction, division, and delivering test
for a 33.28-week embryo which didn't move at the hour of
securing.
Notwithstanding cerebrum and organ volumetry, the
picture handling methods created here can be utilized for

the assessment and appraisal of the shape and design of
fetal body and organs. The most basic issue for the
recreation of the craniofacial structures and other fetal
organs is the non-unbending nature of the movement in
those areas. Assuming the movement is immaterial or on
the other hand in the event that it very well may be
assessed precisely, recreation, division, and perception
will be doable.
REFERENCES
1. Reddy UM, Filly RA, Copel JA (2008) Prenatal imaging:
ultrasonography and magnetic resonance imaging. Obstet
Gynecol 112(1):145–157
2. Coakley FV, Glenn OA, Qayyum A, Barkovich AJ,
Goldstein R, Filly RA (2004) Fetal MRI: a developing
technique for the developing patient. AJR Am J Roentgenol
182(1):243–252
3. Guibaud L (2009) Contribution of fetal cerebral MRI for
diagnosis of structural anomalies. Prenat Diagn 29(4):420–
433
4. Garel C (2008) Imaging the fetus: when does MRI really
help. Pediatr Radiol 3(38 Suppl):S467–S470
5. Garel C (2005) Fetal cerebral biometry: normal
parenchymal findings and ventricular size. Eur Radiol
15(4):809–813
6. Tilea B, Alberti C, Adamsbaum C, Armoogum P, Oury JF,
Cabrol D, Sebag G, Kalifa G, Garel C (2009) Cerebral
biometry in fetal magnetic resonance imaging: new
reference data. Ultrasound Obstet Gynecol 33(2):173–181
7. Chang CH, Yu CH, Chang FM, Ko HC, Chen HY (2003) The
assessment of normal fetal brain volume by 3-D
ultrasound. Ultrasound Med Biol 29(9):1267–1272
8. Kazan-Tannus JF, Dialani V, Kataoka ML, Chiang G,
Feldman HA, Brown JS, Levine D (2007) MR volumetry of
brain and CSF in fetuses referred for ventriculomegaly. AJR
Am J Roentgenol 189(1):145–151
9. Roberts N, Garden AS, Cruz-Orive LM, Whitehouse GH,
Edwards RH (1994) Estimation of fetal volume by
magnetic resonance imaging and stereology. Br J Radiol
67(803):1067–1077
10. Baker PN, Johnson IR, Gowland PA, Hykin J, Adams V,
Mansfield P, Worthington BS (1995) Measurement of fetal
liver, brain and placental volumes with echo-planar
magnetic resonance imaging. Br J Obstet Gynaecol
102(1):35–39
11. Gong QY, Roberts N, Garden AS, Whitehouse GH (1998)
Fetal and fetal brain volume estimation in the third
trimester of human pregnancy using gradient echo MR
imaging. Magn Reson Imaging 16(3):235–240
12. Schierlitz L, Dumanli H, Robinson JN, Burrows PE,
Schreyer AG, Kikinis R, Jolesz FA, Tempany CM (2001)
Three-dimensional magnetic resonance imaging of fetal
brains. Lancet 357:1177–1178
13. Endres LK, Cohen L (2001) Reliability and validity of
threedimensional fetal brain volumes. J Ultrasound Med
20:1265–1269
14. Kinoshita Y, Okudera T, Tsuru E, Yokota A (2001)
Volumetric analysis of the germinal matrix and lateral
ventricles performed using MR images of postmortem
fetuses. AJNR Am J Neuroradiol 22(2):382–388
15. Roelfsema NM, Hop WC, Boito SM, Wladimiroff JW
(2004) Three-dimensional sonographic measurement of
normal fetal brain volume during the second half of
pregnancy. Am J Obstet Gynecol 190(1):275–280
16. Boito S, Struijk PC, Ursem NT, Fedele L, Wladimiroff JW
(2003) Fetal brain/liver volume ratio and umbilical
volume flow parameters relative to normal and abnormal
human development. Ultrasound Obstet Gynecol
21(3):256–261
17. Gholipour A, Warfield SK (2009) Super-resolution
reconstruction of fetal brain MRI. In: Studholme C,
Rousseau F (eds) MICCAI Workshop on Image Analysis for
the Developing Brain (IADB’2009). London, UK, pp 45–52,
24 Sept 2009 [Online]. Available:
http://www.crl.med.harvard.edu/ publications/SR-Fetal-
Brain-MRI-IADB-MICCAI2009.pdf
18. Gholipour A Estroff JA, Warfield SK (2010) Robust
superresolution volume reconstruction from slice
acquisitions: application to fetal brain MRI. IEEE Trans
Med Imaging. doi:10.1109/ TMI.2010.2051680 (in press)
19. Rousseau F, Glenn OA, Iordanova B, Rodriguez-
Carranza C, Vigneron DB, Barkovich JA, Studholme C
(2006) Registration based approach for reconstruction of
high-resolution in utero fetal mr brain images. Acad Radiol
13(9):1072–1081

20. Jiang S, Xue H, Glover A, Rutherford M, Rueckert D,
Hajnal J (2007) MRI of moving subjects using multislice
snapshot images with volume reconstruction (SVR):
application to fetal, neonatal, and adult brain studies. IEEE
Trans Med Imaging 26(7):967–980
21. Prayer D, Brugger PC, Prayer L (2004) Fetal MRI:
techniques and protocols. Pediatr Radiol 34:685–693
22. Lee S, Wolberg G, Shin S (1997) Scattered data
interpolation with multilevel b-splines. IEEE Trans Vis
Comput Graph 3(3):228–244
23. Kim K, Habas PA, Rousseau F, Glenn OA, Barkovich AJ,
Studholme C (2010) Intersection based motion correction
of multislice MRI for 3D in utero fetal brain image
formation. IEEE Trans Med Imag 29(1):146–158
24. Levine D, Barnes PD, Robertson RR, Wong G, Mehta TS
(2003) Fast MR imaging of fetal central nervous system
abnormalities. Radiology 229:51–61
25. Mangin J (2000) Entropy minimization for automatic
correction of intensity nonuniformity. In: Proceedings of
the of the IEEE workshop on mathematical methods in
biomedical image analysis. p 162

Detection of abnormalities in Fetus using Medical Image Processing

Recommended

Recommended

More Related Content

Similar to Detection of abnormalities in Fetus using Medical Image Processing

Similar to Detection of abnormalities in Fetus using Medical Image Processing (20)

More from IRJET Journal

More from IRJET Journal (20)

Recently uploaded

Recently uploaded (20)

Detection of abnormalities in Fetus using Medical Image Processing