Paper Info
Title
Detection of the features of the objects in MR images using dynamic programming
Abstract
Prostate cancer is the most frequently diagnosed cancer in males. It is also the second leading cause of cancer-related deaths in males after lung cancer. The treatment for patients with prostate cancer requires the diagnosis of the exact localization of the cancer. However, such a treatment remains a challenge, since the tumor biology of prostate cancer is still poorly understood. In the recent time, since developed imaging technologies, there are several methods have been developed to diagnose the prostate cancer based on the image. They allow understand more clearly the tumor biology of prostate cancer. In this paper, we propose to apply dynamic programming to detect the objects (tumors) in prostate images (MRI - magnetic resonance image) based on its features. Dynamic programming is one of the most powerful tools in optimization. We propose to use this technique to minimize the distance between active contours and the region representing the tumor. The challenge of this application is due to the noise and difficulties to capture the prostate tumor with high resolution and good contrast. The only solution to apply active contours which could interpolate between points converging on the region of interest. Dynamic programming takes into account the constraints set up by the user or by the environmental conditions related to image acquisition.
Year
DOI
Venue
2014
10.1109/IPTA.2014.7001991
Image Processing Theory, Tools and Applications
Keywords
Field
DocType
biomedical MRI,cancer,dynamic programming,feature extraction,medical image processing,object detection,tumours,MR images,cancer-related deaths,distance minimization,dynamic programming,image acquisition,lung cancer,magnetic resonance image,object feature detection,prostate cancer diagnosis,region-of-interest,tumor biology,Active contours,MRI,dynamic programming,prostate cancer
Lung cancer,Dynamic programming,Computer vision,Pattern recognition,Computer science,Image segmentation,Prostate cancer,Artificial intelligence,Prostate,Region of interest,Cancer,Magnetic resonance imaging
Conference
ISSN
Citations 
PageRank 
2154-512X
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Minh Hoan Pham110.75
Doncescu, A.28625.70