tailieunhanh - Effect of morphological algorithms on medical imaging

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). | Nuclear Science and Technology No. 3 2018 pp. 01-08 Effect of morphological algorithms on medical imaging Phan Viet C uong1 Ho Thi Thao2 Le Tuan Anh1 Nguyen Hong Ha2 Ha Quang Thanh3 1 Vietnam Atomic Energy Institute 2Centre of Nuclear Physics Institute of Physics Vietnam Academy of Science and Technology Hanoi Vietnam 3 National Institute of Medical Device and Construction Corresponding author pvcuong@ Received 16 November 2018 accepted 28 November 2018 Abstract Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article we focus on describing the new morphological algorithms by ITK Insight Segmentation and Registration Toolkit . These morphological operators eliminate noise detect good edges and overcome the drawback of traditional edge detection methods. Keywords Medical image processing edge detection image enhancement morphological algorithms ITK. I. INTRODUCTION Most of the medical images X-ray CT-Computed tomography and MRI- Magnetic resonance imaging have very low contrast and its grayscale values corresponding to the same tissue change dramatically in comparison to conventional image formats. Because many objects are obscured or invaded by neighboring tissues it is difficult to distinguish the edges of the object of interest and its surroundings. Another cause one of the most common degradations in medical images is noise 1 . Image processing technology in health has attracted many researches in recent times. Classic edge detection methods in 2 3 4 5 using first and second derivatives detect good edges but sensitive to noise. Ishani Thakur and Manish Kansal 6 have summarized various methods for reducing noise on medical images. The method proposed by Rohini Paul Joseph et al. 7 used a series of algorithms to detect and extract brain tumors on MRI. The edges are defined by the gradient and K-mean methods. But this approach is .