PMID- 33265817
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240329
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 20
IP  - 10
DP  - 2018 Sep 23
TI  - Image Thresholding Segmentation on Quantum State Space.
LID - 10.3390/e20100728 [doi]
LID - 728
AB  - Aiming to implement image segmentation precisely and efficiently, we exploit new 
      ways to encode images and achieve the optimal thresholding on quantum state 
      space. Firstly, the state vector and density matrix are adopted for the 
      representation of pixel intensities and their probability distribution, 
      respectively. Then, the method based on global quantum entropy maximization 
      (GQEM) is proposed, which has an equivalent object function to Otsu's, but gives 
      a more explicit physical interpretation of image thresholding in the language of 
      quantum mechanics. To reduce the time consumption for searching for optimal 
      thresholds, the method of quantum lossy-encoding-based entropy maximization 
      (QLEEM) is presented, in which the eigenvalues of density matrices can give 
      direct clues for thresholding, and then, the process of optimal searching can be 
      avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the 
      upper bound of the thresholding level can be implicitly determined according to 
      the eigenvalues; and (2) the proposed approaches ensure that the local 
      information in images is retained as much as possible, and simultaneously, the 
      inter-class separability is maximized in the segmented images. Both of them 
      contribute to the structural characteristics of images, which the human visual 
      system is highly adapted to extract. Experimental results show that the proposed 
      methods are able to achieve a competitive quality of thresholding and the fastest 
      computation speed compared with the state-of-the-art methods.
FAU - Wang, Xiangluo
AU  - Wang X
AD  - School of Information Technology, Luoyang Normal University, Luoyang 471934, 
      China.
FAU - Yang, Chunlei
AU  - Yang C
AD  - School of Information Engineering, Henan University of Science and Technology, 
      Luoyang 471023, China.
FAU - Xie, Guo-Sen
AU  - Xie GS
AD  - School of Information Engineering, Henan University of Science and Technology, 
      Luoyang 471023, China.
FAU - Liu, Zhonghua
AU  - Liu Z
AD  - School of Information Engineering, Henan University of Science and Technology, 
      Luoyang 471023, China.
LA  - eng
PT  - Journal Article
DEP - 20180923
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC7512291
OTO - NOTNLM
OT  - density matrix
OT  - image segmentation
OT  - thresholding
OT  - von Neumann entropy
COIS- The authors declare no conflict of interest.
EDAT- 2018/09/23 00:00
MHDA- 2018/09/23 00:01
PMCR- 2018/09/23
CRDT- 2020/12/03 01:04
PHST- 2018/08/05 00:00 [received]
PHST- 2018/09/11 00:00 [revised]
PHST- 2018/09/20 00:00 [accepted]
PHST- 2020/12/03 01:04 [entrez]
PHST- 2018/09/23 00:00 [pubmed]
PHST- 2018/09/23 00:01 [medline]
PHST- 2018/09/23 00:00 [pmc-release]
AID - e20100728 [pii]
AID - entropy-20-00728 [pii]
AID - 10.3390/e20100728 [doi]
PST - epublish
SO  - Entropy (Basel). 2018 Sep 23;20(10):728. doi: 10.3390/e20100728.