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Object contour based on improving region growing method and context border condition
Author(s) -
Binh Nguyen Thanh,
Hong Tuyet Vo Thi,
Thanh Nguyen Chi
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5472
Subject(s) - artificial intelligence , computer vision , object (grammar) , context (archaeology) , computer science , quality (philosophy) , domain (mathematical analysis) , image quality , medical imaging , image (mathematics) , geography , mathematics , archaeology , mathematical analysis , philosophy , epistemology
Summary Image diagnosis is a very significant medical specialty. Thereby, the doctor will see the form and function of the structure inside the body. The recorded images will be an important basis to help doctors find the disease and treatment more effectively. However, the quality of medical images is a vital factor. Improving the quality of medical images and object contour are the major challenges, because each tiny detail of medical images can contain much information. Object contour in good medical image quality is hard work and it is more difficult in low‐quality medical image. The organs in the image are not clear, which is difficult for the doctor to distinguish the border of the organs in the image. In this paper, we proposed a method for object contour based on improving region growing in bandelet domain in low‐quality medical images. The method includes two stages: increasing the quality of medical image in bandelet domain and object contour by improving region growing combined with context border condition in medical images in the first stage. The result of the proposed method is better than the other methods.