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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Breast cancer,Mammogram,k-means,SVM
- چکیده:
- چکیده انگلیسی: Cancer, a disease of cells, causes cell growth which differs from normal cell growth ratio, this cell growth spreads
in the human body and kills the body cells. Breast cancer, it’s a highly heterogeneous disease and western women
commonly witness this. Mammography, a pre-screening X-ray based check is used to diagnose woman’s breast cancer.
This basic test mode helps in identifying breast cancer at early stage and this early stage detection would support in
recovering more number of women from this serious disease. Medical centres deputed highly skilled radiologists and
they were given the responsibility of analysing this mammography results but still human errors are inevitable. An error
frequency ratio is high when radiologists exhausted in their analysis task and leads variations in either observations
ie., internal or external observation. Also, quality of the image plays vital role in Mammographic sensitivity and leads
to variation. Several automation processes were tried in streamlining and standardising diagnosis analysis process and
quality of breast cancer images were improved. This paper inducts a two way mode algorithm for grouping of breast
cancer images to 1. benign (tumour growing, but not dangerous) and 2. malignant (cannot be controlled, it causes death)
classes. Two-way mode data mining algorithms are used due to thinly dispersed distribution of abnormal mammograms.
First type algorithm is k-means algorithm, which regroups the given data elements into clusters (ie., prioritized by the
users). Second type algorithm is Support Vector Machine (SVM), which is used to identify the most suitable function
which differentiates the members based on the training data.- انتشار مقاله: 10-12-1396
- نویسندگان: Sinthia P,Malathi M
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: K means clustering,Fuzzy C means clustering,Spatial fuzzy C means,Discrete Wavelet transform,Back propagation algorithm
- چکیده:
- چکیده انگلیسی: Generally the segmentation refers, the partitioning of an image into smaller regions to identify or locate the region of
abnormality. Even though image segmentation is the challenging task in medical applications, due to contrary image,
local observations of an image, noise image, non uniform texture of the images and so on. Many techniques are available
for image segmentation, but still it requires to introduce an efficient, fast medical image segmentation methods. This
research article introduces an efficient image segmentation method based on K means clustering integrated with
a spatial Fuzzy C means clustering algorithms. The suggested technique combines the advantages of the two methods.
K means segmentation requires minimum computation time, but spatial Fuzzy C means provides high accuracy for
image segmentation. The performance of the proposed method is evaluated in terms of accuracy, PSNR and processing
time. It also provides good implementation results for MRI brain image segmentation with high accuracy and minimal
execution time. After completing the segmentation the of abnormal part of the input MRI brain image, it is compulsory
to classify the image is normal or abnormal. There are many classifiers like a self organizing map, Back propagation
algorithm, support vector machine etc., The algorithm helps to classify the abnormalities like benign or malignant brain
tumour in case of MRI brain image. The abnormality is detected based on the extracted features from an input image.
Discrete wavelet transform helps to find the hidden information from the MRI brain image. The extracted features are
trained by Back Propagation Algorithm to classify the abnormalities of MRI brain image.- انتشار مقاله: 24-03-1397
- نویسندگان: Malathi M,Sinthia P
- مشاهده