Open Access
ARTICLE
Adaptive Image Enhancement Using Hybrid Particle Swarm Optimization and Watershed Segmentation
N. Mohanapriya1, Dr. B. Kalaavathi2
1Assistant Professor / CSE, Vivekanandha College of Engineering for Women, Tiruchengode, Namakkal-637 205, Tamilnadu, India.
2 Professor / CSE, K.S.R Institute for Engineering and Technology, Tiruchengode, Namakkal-637 215, Tamilnadu, India.
* Corresponding Author: N. Mohanapriya,
Intelligent Automation & Soft Computing 2019, 25(4), 663-672. https://doi.org/10.31209/2018.100000041
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.
Keywords
Cite This Article
APA Style
Mohanapriya, N., Kalaavathi, D.B. (2019). Adaptive image enhancement using hybrid particle swarm optimization and watershed segmentation. Intelligent Automation & Soft Computing, 25(4), 663-672. https://doi.org/10.31209/2018.100000041
Vancouver Style
Mohanapriya N, Kalaavathi DB. Adaptive image enhancement using hybrid particle swarm optimization and watershed segmentation. Intell Automat Soft Comput . 2019;25(4):663-672 https://doi.org/10.31209/2018.100000041
IEEE Style
N. Mohanapriya and D.B. Kalaavathi, "Adaptive Image Enhancement Using Hybrid Particle Swarm Optimization and Watershed Segmentation," Intell. Automat. Soft Comput. , vol. 25, no. 4, pp. 663-672. 2019. https://doi.org/10.31209/2018.100000041