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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

Flow chart of enhancement processes
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Fig2: Flow chart of enhancement processes

Mentions: This study proposes an adaptive algorithm for brain MRI images called the hierarchical correlation histogram analysis algorithm (HCHA). HCHA can automatically enhance three types of major PD-affected brains atrophic cells in images. This algorithm uses each object’s grayscale distribution degree of pixel intensity to construct a correlation histogram matrix from the original ROI. Then, it generates a segment of the correlation histogram matrix wherein different blocks express the correlation distribution degree of each object, since the specific objects cannot be represented in the global correlation histogram. The hierarchical analysis focuses on specific objects to represent the correlation distributions of pixels for optimization and adaptively regulates the contrast of each specific object. Figure 2 shows the overall flow chart of the proposed method.Fig. 2


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)

Flow chart of enhancement processes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Flow chart of enhancement processes
Mentions: This study proposes an adaptive algorithm for brain MRI images called the hierarchical correlation histogram analysis algorithm (HCHA). HCHA can automatically enhance three types of major PD-affected brains atrophic cells in images. This algorithm uses each object’s grayscale distribution degree of pixel intensity to construct a correlation histogram matrix from the original ROI. Then, it generates a segment of the correlation histogram matrix wherein different blocks express the correlation distribution degree of each object, since the specific objects cannot be represented in the global correlation histogram. The hierarchical analysis focuses on specific objects to represent the correlation distributions of pixels for optimization and adaptively regulates the contrast of each specific object. Figure 2 shows the overall flow chart of the proposed method.Fig. 2

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