A Fully Automatic Scheme for Medical Image Segmentation with Wavelet Based Image Fusion

Medical image segmentation has become an essential technique in clinical and researchoriented applications. Because manual segmentation methods are tedious, and semi automatic segmentation lacks the flexibility, fully-automatic methods have become the preferred type of medical image segmentation. This work proposes a robust fully automatic segmentation scheme based on the modified contouring technique. The entire scheme consists of three stages. In the first stage, the wavelet transform of image is computed. This is followed by the fusion of LH, HL and HH coefficients (Method-II) or fusion of two different source images (Method-I). For that fused image local threshold is computed. This is followed by the second stage in which the initial points are determined by computation of global threshold. Finally, in the third stage, searching procedure is started from each initial point to obtain closed-loop contours. The whole process is fully automatic. This avoids the disadvantages of semi-automatic schemes such as manually selecting the initial contours and points.