Model-based Image Segmentation
Image de-noising and image segmentation represent a crucial
part in many image processing and computer vision
applications. The goal of de-noising is to smooth homogenous
regions of an image while preserving the region boundaries
(i.e., edges). Meanwhile, segmentation techniques aim to
extract the boundaries of homogenous regions. In traditional
pipelined de-noising/segmentation schemes, the segmentation
step may suffer from a loss of vital information due to the
de-noising step, or due to the excess of noise. Combining
these two steps should preserve much of this lost information
compared to the traditional methods.
In this research, we plan to combine speckle image de-noising
and image segmentation processes in an iterative fashion at
various levels of granularity, rather than simultaneously.
This way, de-noising and segmentation sub-results would be
utilized by both schemes at all levels. This should
facilitate the ability to smooth irrelevant details from the
image while enhancing and successfully segmenting the desired
object. In addition, we plan to incorporate prior knowledge
and optical flow information to the image
segmentation/de-noising process. This direction should
improve the accuracy of the produced segmentation results,
especially when the object of interest has weak edges. We
will also plan to improve the de-noising diffusion process by
adaptively calculating diffusion coefficients. This step
would improve de-noising results at a moderate complexity.
The proposed combined segmentation-diffusion approach will be
applied on real echocardiographic and carotid ultrasound
images. These images tend to be plagued by speckle noise and
other anomalous artifacts due to the sporadic nature of high
frequency sound waves. Hence, they present a perfect
environment for validating and testing our proposed research.
Our proposed schemes would help improve the reliability of
various clinical measures such as a left ventricular ejection
fraction or a carotid artery stenosis assessment. The
proposed segmentation schemes will be also validated and
tested on synthetic images with simulated speckle noise.