@Article{2018.100000041,
AUTHOR = {N. Mohanapriya, Dr. B. Kalaavathi},
TITLE = {Adaptive Image Enhancement Using Hybrid Particle Swarm  Optimization and Watershed Segmentation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {4},
PAGES = {663--672},
URL = {http://www.techscience.com/iasc/v25n4/39695},
ISSN = {2326-005X},
ABSTRACT = {Medical images are obtained straight from the medical acquisition devices so 
that, the image quality becomes poor and may contain noises. Low contrast 
and poor quality are the major issues in the production of medical images. 
Medical imaging enhancement technology gives way to solve these issues; it 
helps the doctors to see the interior portions of the body for early diagnosis, 
also it improves the features the visual aspects of an image for a right 
diagnosis. This paper proposes a new blend of Particle Swarm Optimization 
(PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid Partial 
Swarm Optimization (HPSO) to enhance medical images and also gives optimal 
results. The work starts with (i) watershed segmentation followed by (ii) HPSO 
enhancement algorithm. The watershed segmentation is a morphological 
gradient-based transformation technique. The gradient map of an image has 
different gradient values corresponds to different heights. It extracts the 
continuous boundaries of each region to give solid results and intuitively 
provides better performance on noisy images. After segmentation, the HPSO 
algorithm is applied to improve the quality of Computed Tomography (CT) 
images by calculating the local and global information. The transformation 
function uses the calculated information to optimize the medical image. The 
algorithm is tested on a real time data set of CT images, which were collected 
from MIT-BIH dataset and the performance is analyzed and compared with 
existing Region Merging (RM), Fuzzy C Means (FCM), Histogram Thresholding, 
Discrete Wavelet Transformation (DWT), Particle Swarm Optimization (PSO), 
Artificial Bee Colony (ABC), Histogram Equalization (HE), Contrast Stretching 
and Adaptive Filtering based on PSNR, SSIM, CII, MSE, RMSE, BER and 
Execution time parameters. The experimental result shows that the proposed 
medical image enhancement algorithm achieves 96.7% accuracy and defeat the 
over segmentation problem of existing systems.},
DOI = {10.31209/2018.100000041}
}