Introduction:
Image segmentation is one of the basic practices that involve dividing an
image into mutually exclusive partitions. Learning how to partition an image into different segments
is considered as one of the most critical and crucial step in the area of medical image analysis.
Objective:
The primary objective of the work is to design an integrated approach for automating the
process of level set segmentation for medical image segmentation. This method will help to overcome
the problem of manual initialization of parameters.
Methods:
In the proposed method, input image is simplified by the process of intuitionistic fuzzification
of an image. Further segmentation is done by intuitionistic based clustering technique incorporated
with local spatial information (S-IFCM). The controlling parameters of level set method are
automated by S-IFCM, for defining anatomical boundaries.
Results:
Experimental results were carried out on MRI and CT-scan images of brain and liver. The
results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation
using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed
segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value
of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the
segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value.
Conclusion:
The proposed method shows good accuracy for the segmentation of medical images.
This method is a good substitute for the segmentation of different clinical images with different modalities
and proves to give better result than fuzzy technique.
An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction
