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Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis.

Chen CM, Chen CC, Wu MC, Horng G, Wu HC, Hsueh SH, Ho HY - J Med Biol Eng (2015)

Bottom Line: The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy.The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values.Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617 Taiwan.

ABSTRACT

Parkinson's disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

No MeSH data available.


Related in: MedlinePlus

a HE result, b opening result, c closing result, d CLAHE result, and e proposed method of the boundary detection result in enhanced images
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Fig6: a HE result, b opening result, c closing result, d CLAHE result, and e proposed method of the boundary detection result in enhanced images

Mentions: In Table 2, HE and CLAHE appear to be better than the proposed method because there is more edge information in the image, but there is no focus on prominent useful edges. The opening and closing methods cannot represent information related to edges. Although the result of the proposed method is inferior to those of the HE and CLAHE methods, this method can create prominent edges of specific objects. The results are significantly better than those obtained with other methods, as shown in Fig. 6.Fig. 6


Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis.

Chen CM, Chen CC, Wu MC, Horng G, Wu HC, Hsueh SH, Ho HY - J Med Biol Eng (2015)

a HE result, b opening result, c closing result, d CLAHE result, and e proposed method of the boundary detection result in enhanced images
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4666237&req=5

Fig6: a HE result, b opening result, c closing result, d CLAHE result, and e proposed method of the boundary detection result in enhanced images
Mentions: In Table 2, HE and CLAHE appear to be better than the proposed method because there is more edge information in the image, but there is no focus on prominent useful edges. The opening and closing methods cannot represent information related to edges. Although the result of the proposed method is inferior to those of the HE and CLAHE methods, this method can create prominent edges of specific objects. The results are significantly better than those obtained with other methods, as shown in Fig. 6.Fig. 6

Bottom Line: The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy.The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values.Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617 Taiwan.

ABSTRACT

Parkinson's disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

No MeSH data available.


Related in: MedlinePlus