Feature based transition region extraction for image segmentation: Application to worm separation from leaves

Abstract

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed transition region extraction method for image segmentation. The proposed method initially decomposes the gray image in wavelet domain. Local standard deviation filtering and thresholding operation is used to extract transition region feature matrix. Using this feature matrix, the corresponding prominent wavelet coefficients of different bands are found. The inverse wavelet transform is then applied to the modified coefficients to get edge image with more than one-pixel width. Global thresholding is applied to get transition regions. Further, it undergoes morphological thinning and region filling operation to extract the object regions. Finally, the objects are extracted using the object regions. The proposed method is compared with different image segmentation methods. An experimental result reveals that the proposed method outperforms other methods for segmentation of images containing single and multiple objects. The proposed method can also be applied for worm separation from leaves.