This Paper offers an effective method to measure the length of the
femur in Fetal Ultrasound Images, it applies a series of steps
starting with the reducing amount of noise in these images, and
then converted them to a binary form and uses morphol
ogical
operations to segment the femur and isolate it from the rest of the
image objects, then it applies an Edge Detector in order to find the
edges of the bone, then uses the Hough Transform to detect straight
lines in the image. we apply overlapping for resulted lines on the
original image, finally we choose the most significant and longest
straight line which is corresponding to the length of the femur. The
proposed method facilitates the measurement of the femur without
the help of a physician through a series of steps.
This Paper offers an innovative way for auto segmentation of the fetal head in
ultrasound US images. There is high amount of noise in US images, which it affects the
visual appearance of the area of head. The research depends on auto segmentation
mechanism without the need for user intervention at any stage of proposed method, so this
is what makes segmentation process is difficult and important at the same, because the
weakness of the edges and not fully enclosed in the desired region. We relied on a Level
Set method to segment the head area, after determining the initial contour automatically by
the Region Properties Function. The proposed method proves effective in the head area
segmentation without being influenced by noise or the existence of discontinuities in the
edges of the head, despite the absence of a pre-processing stage in a series of steps applied
to several ultrasound images in different sizes and sources. The last step is to calculate the
secondary diameter of the output ellipse (the fetal head sector) depending on the properties
of the region, and this final measurement represents the Bi Parietal Diameter BPD, an
important measure enables the physician to assess gestational age and determine the birth
of the fetus date. Segmentation result has been authenticated based on similarity criteria,
and the final measurement accuracy has been compared with manual measurements carried
out by a specialist. The comparison results showed the effectiveness of the proposed
algorithm and its success by up to 98%.
