Spatial Fuzzy C-means dan Rapid Region Merging untuk Pemisahan Sel Kanker Payudara
Abstract: Segmentation and
overlapped cells separation are important phases in microscopic image
processing of breast cancer, because the accuracy of overlapped cells
separation result determines the accuracy of breast cancer cell calculation.
The amount of breast cancer cells is considered by doctor in determining the
action towards patients. Two of the most common topics discussed in previous
studies are the problem of increasing the accuracy of overlapped cancer cell
separation result by calculating the number of cancer cell and
over-segmentation problem. Compared to watershed method, clustering method
produces higher accuracy in separating overlapped cancer cells. In this paper,
a combination of Spatial Fuzzy C-Means (SFCM) and Rapid Region Merging (RRM)
method is proposed to separate the overlapped cells and handling the
over-segmentation problem. The input image of overlapped cells separation phase
is the result of breast cancer cell identification by Gram-Schmidt (GS) method,
while the clustered cancer cells are overlapped cancer cells which are detected
based on the area of geometric feature. 40 microscopic breast cancer cells
image of benign and malignant type is used as the datasets. The average value
of Mean Square Error (MSE) for cell identification is 0.07 and the average
accuracy of overlapped cells separation using SFCM and RRM is 78.41%.
Kata Kunci: Segmentasi Sel Bertumpuk, Citra Mikroskopis, Sel Kanker
Payudara, Spatial Fuzzy C-Means, Rapid Region Merging
Penulis: Desmin Tuwohingide,
Chastine Fatichah
Kode Jurnal: jptlisetrodd170184
