Liver Segmentation using Bidirectional Region Growing with Edge Enhancement in NSCT Domain

Segmentation of liver from abdominal CT image seems to be a challenging task in the computer aided diagnosis of liver pathology. This is due to the fact that liver and the adjacent organs in the CT image possess similar intensity attributes. In this work, a semi-automatic segmentation of liver is carried out through bidirectional region based segmentation. Prior to segmentation, the edge regions of the liver are enhanced by means of unsharp masking in Non Sub-sampled Contourlet Transform (NSCT) domain. This ensures that there is a precise differentiation between the liver region and the adjacent organs and other structures like ribs. From the acquired results, it is clear that the combination of bidirectional region growing segmentation and edge enhancement in NSCT domain outperforms the segmentation by region growing algorithm and bidirectional region growing algorithm without edge enhancement.

[1]  Carlos López-Martínez,et al.  Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[3]  Klaus D. Tönnies,et al.  Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.

[4]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[5]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[6]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  Scott T. Acton,et al.  Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials , 2015, IEEE Signal Processing Letters.

[8]  Éloi Bossé,et al.  An iterative possibilistic knowledge diffusion approach for blind medical image segmentation , 2018, Pattern Recognit..

[9]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[10]  Imen Karoui,et al.  Variational Region-Based Segmentation Using Multiple Texture Statistics , 2010, IEEE Transactions on Image Processing.

[11]  Shanq-Jang Ruan,et al.  Low order adaptive region growing for lung segmentation on plain chest radiographs , 2018, Neurocomputing.

[12]  Aly A. Farag,et al.  A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets , 2013, IEEE Transactions on Image Processing.

[13]  Yadong Wang,et al.  Accurate Vessel Segmentation With Constrained B-Snake , 2015, IEEE Transactions on Image Processing.

[14]  M. A. Sid-Ahmed,et al.  Edge enhancement in digital images using phase contrast filtering , 1990, Canadian Journal of Electrical and Computer Engineering.