Region growing, in a simple version, is a segmentation process, which having pixels as seeds, pixels with the same intensities and connected them are added to the area gradually, and finally presents a binary image that contains the object or objects of the target. So far, many binary segmentation techniques have been developed to extract target objects, with the common disadvantage that they do not perform the extraction task completely. Frames as the generalization of orthogonal bases are used scarcely in these algorithms. In this paper, a new nonlinear contrast stretching function is introduced, and then, based on the contrast stretching function andshearlets frame, correct initialization seeds are extractedand then the region growing algorithm applyto the image. The results presented on synthetic images and real medical images show the advantages of our technique to those recently proposed.
Mirzafam, M., & Aghazadeh, N. (2021). Blood Vessels Extraction from MRA Images by a Region Growing Algorithm Based on a New Nonlinear Contrast Stretching Function and Shearlets Frame. Journal of Machine Vision and Image Processing, 8(2), 85-99.
MLA
Mehdi Mirzafam; Nasser Aghazadeh. "Blood Vessels Extraction from MRA Images by a Region Growing Algorithm Based on a New Nonlinear Contrast Stretching Function and Shearlets Frame". Journal of Machine Vision and Image Processing, 8, 2, 2021, 85-99.
HARVARD
Mirzafam, M., Aghazadeh, N. (2021). 'Blood Vessels Extraction from MRA Images by a Region Growing Algorithm Based on a New Nonlinear Contrast Stretching Function and Shearlets Frame', Journal of Machine Vision and Image Processing, 8(2), pp. 85-99.
VANCOUVER
Mirzafam, M., Aghazadeh, N. Blood Vessels Extraction from MRA Images by a Region Growing Algorithm Based on a New Nonlinear Contrast Stretching Function and Shearlets Frame. Journal of Machine Vision and Image Processing, 2021; 8(2): 85-99.
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