Many
methods have been proposed for MRI brain tissue segmentation. Since the
boundaries of different tissues in MRI brain images are indistinct and
the intensities of the white and gray matter are very close, the
edge-based segmentation methods may not provide satisfactory results.
Thus the knowledge-based methods, e.g. clustering algorithms for brain
tissue classification, integrated edge and region detection, and
deformable template-based methods are being investigated for automatic
and accurate image segmentation. Knowledge-based methods include several
pattern recognition procedures needing a priori anatomical knowledge of
the region-of-interest and the human training data set. Clustering
methods such as Kmeans and Fuzzy C-means do not always arrive at a
meaningful segmentation, and often require long computational time.
Effective segmentation is required. For MRI image segmentation, it is
important to identify anatomical areas of interest for diagnosis. In
this context, we are more concerned with the anatomical region or
structure segmentation. We know that the intensity of the MRI image of
human tissue is homogeneous and the structure of each tissue is
connected, but it is difficult to separate the adjacent tissues due to
the small intensity changes and smoothed boundaries between the tissues.
The intensity-based segmentation using global thresholding can not
segment MRI images properly because of the non-uniform nature of the
MRI. Combining both spatial and intensity
information in the image, this design based on an MRI brain image
segmentation approach based on multi-resolution edge detection, region
selection, and intensity threshold methods. The detection of white
matter structure in the brain is emphasized in this project. First, a
multi-resolution brain image representation and segmentation procedure
based on a multi-scale image filtering method are presented. Given the
nature of the structural connectivity and intensity homogeneity of brain
tissues, region-based methods such as region growing and subtraction to
segment the brain tissue structure from the multi-resolution images are
utilized. From the segmented structure, the region-of-interest (ROI)
image in the structure region is derived, and then a modified
segmentation of the ROI based on an automatic threshold method using our
threshold selection criterion is presented. Examples on both T1 and T2
weighted MRI brain image segmentation is presented, showing finer brain
tissue structures.
Reference Paper: MRI brain image segmentation by multi-resolution edge detection and region selection Author’s Name: H. Tang, E.X. Wua, Q.Y. Ma, D. Gallagher, G.M. Perera, and T. Zhuang Source: Elsevier Year:2004 Request source code for academic purpose, fill REQUEST FORM or contact +91 7904568456 by WhatsApp or sales@verilogcourseteam.com, fee applicable. SIMULATION VIDEO DEMO

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