@article{MAKHILLJEAS2019141518142,
    title = {Automatic Segmentation of Breast Mammograms Using Hybrid Density
Slicing and k-mean Adaptive Methods},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {15},
    pages = {5044-5050},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.5044.5050},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.5044.5050},
    author = {Semaa and},
    keywords = {Mammograms,breast cancer,image segmentation,image processing,adaptive,k-means and
density slicing},
    abstract = {<p>Medical imaging is a fundamental theme of contemporary healthcare and its engineers take
mammograms, ultrasounds, X-rays and computed tomography images to analyze patient&rsquo;s hurts and illnesses.
Segmenting the mammogram into diverse mammographic densities is strategic for risk evaluation and
measurable appraisal of density variations to extract the cancer regions. Accordingly in this study, the
application of density slicing and k-mean adaptive techniques has been conducted to explore the boundary of
changed breast tissue areas in mammograms. The objective of the segmentation process is to perceive if density
slicing and k-mean adaptive procedure have the feasibility split diverse densities for the diverse breast outlines.
The density slicing is used to make available hard limitation while the thresholds are designated in accordance
with user-defined and radiology. k-mean adaptive has been used to cluster the region where the initial seed was
based on the mean of array multiply by 0.05. Density slicing has processed on images of numerous imaging
modalities without mammograms consideration. As a result, this study is for all intents and purposes
concentrated on using hybrid method of density slicing and k-mean adaptive process to achieve segmentation
to augment the discernibility of diverse breast densities in mammography images. The suggested approach for
the segmentation of mammograms on the source of their region into diverse densities based classifications has
been investigated on mini-MIAS database. The concluding consequences show instinctive segmentation of
ROI with edge map and dissimilar properties extraction for the investigation process.</p>}
    }