KLASIFIKASI KANKER PADA CITRA MAMMOGRAM BERDASARKAN FITUR BENTUK
Breast cancer is one of the most dangerous types of cancer that affects women all over the world. Based on characteristic mammography image a physician or radiologist can find change in the breast. Analysis of mammography image performed by radiologist still done manually. So the result of the analy...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Theses and Dissertations NonPeerReviewed |
| منشور في: |
[Yogyakarta] : Universitas Gadjah Mada
2013
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://repository.ugm.ac.id/126177/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=66375 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
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| الملخص: | Breast cancer is one of the most dangerous types of cancer that affects
women all over the world. Based on characteristic mammography image a physician
or radiologist can find change in the breast. Analysis of mammography image
performed by radiologist still done manually. So the result of the analysis of radiological
or doctor are subjective, in addition to the results of the observation of
doctor sometimes take a long time and also the result obtained sometimes not appropriate.
Therefore we need a tool that can accelerate radiologist to performance
using digital image processing techniques.
In this study desined a system that can help the radiologist in the classification
of cancer on the mammogram image. This process begins by elimination
of the background and label image, then segmentation of pectoral muscle, the next
process is to improving the quality of the image, the next process is segmentation
of suspected area cancer, the next process is feature extraction using invariant moment
and the final process is to classification using KNN method. This process will
determine the classification of mammogram image into normal classes, benign, and
malignant.
Data used in this study take from MIAS database totaling 126 image consisting
of 40 normal, 48 benign, and 38 malignant. Accuracy obtained 76,9% for
class normal and abnormal when the value of k = 9. While the average accuracy
obtained amounted 50,39% to classification into class benign and class malignant. |
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