@article{MAKHILLAJIT201615216498,
    title = {Threshold Based Lung Image Segmentation with Robust Artificial Bee Colony
Algorithm Optimization Technique},
    journal = {Asian Journal of Information Technology},
    volume = {15},
    number = {21},
    pages = {4426-4430},
    year = {2016},
    issn = {1682-3915},
    doi = {ajit.2016.4426.4430},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2016.4426.4430},
    author = {K. Senthil,K. and},
    keywords = {Thresholding,image segmentation,RGB to Gray,Artificial Bee Colony Algorithm (ABC),,fitness and uniformity},
    abstract = {Image segmentation is a complex task which helps us to extract information for analysis a digital
image. Millions of methods are available for image segmentation. Out of that image thresholding is a simple,
efficient and frequently adopted method for image segmentation. Thresholding basically divide a digital image
into two regions; foreground and background based on the intensity value of the pixels. The key point in image
thresholding is on the optimum value of threshold of the digital image. It is an important and crucial task to
select the optimum threshold. A false choice of threshold will lead to poor results in image segmentation.
Generally optimization algorithms are used to select the optimum threshold value. Artificial Bee Colony (ABC)
algorithm is one of the optimization algorithms which are the replica of natural behaviour of honey bees to find
abundant nectar amount. This study describes an approach to segment an 8 bit human lung image using
artificial bee colony algorithm based thresholding method. The proposed method proves that the uniformity
factor in the image segmentation is good relative to other conventional methods.}
    }