@article{MAKHILLIJSC201813321446,
    title = {Detection of Bone Cancer Using Region Growing Algorithm and
Mean Pixel Intensity Thresholding},
    journal = {International Journal of Soft Computing},
    volume = {13},
    number = {3},
    pages = {69-80},
    year = {2018},
    issn = {1816-9503},
    doi = {ijscomp.2018.69.80},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2018.69.80},
    author = {Madhuri,L.V. and},
    keywords = {Cancer,disease,values,classification,accuracy,analysis},
    abstract = {Cancer, a condition involving unregulated cell growth is a dangerous disease. After many research
studies, almost 100 different types of cancers that occur in the human body have been detected. Of these, one
of the most common is bone cancer which leads to death. The detection of bone cancer is very arduous and
its occurrence is unpredictable. Currently, most studies on cancer exploit data mining methods and the image
processing techniques used for medical image analysis. The data and knowledge collected from large databases
and related web sites have been used by many scientific researchers for formulating predictions. Association
rule mining Supports Vector Machines (SVMs), fuzzy theory and probabilistic neural networks, learning vector
quantization, k-means and C-means are the methods most frequently used for the detection, classification and
segmentation of bone cancer. In this study, a region growing algorithm has been applied for bone image
segmentation. The segmented image is further processed to provide bone cancer detection by evaluating the
mean pixel intensity of identified area. Threshold values are proposed for the classification of medical images
according to the presence or the absence of bone cancer. This method utilizes jpeg images but can also be
applied to medical images in the original format of DICOM (Digital Imaging Communication of Medicine) after
some modification. The method provides an accuracy rate of 100% and the required computation time is
relatively short.}
    }