BibTex Citation Data :
@article{BFIS60737, author = {Ahmad Tahier and Aditya Hariyanto and Much Azam}, title = {DETEKSI KANKER PARU MENGGUNAKAN SEGMENTASI CITRA CT- SCAN DENGAN ALGORITMA KLUSTER K-MEANS}, journal = {BERKALA FISIKA}, volume = {26}, number = {2}, year = {2023}, keywords = {Ccomputed Tomography, Thresholding Segmentation, K-means, breast cancer}, abstract = {Imaging technology such as Computed Tomography (CT) plays an important role in producing high-resolution images. CT makes it easy for an oncologist to diagnose patients for malignant and benign tumors. However, the technology requires image engineering techniques to make the diagnosis more accurate. The technique to draw that is segmentation. Segmentation plays an important role to distinguish between healthy tissue, benign and malignant tumors. There are segmentation techniques based on mass density (pixel), contour (color) etc. This article will review and explain the development of segmentation techniques specific to lung cancer images. Thresholding segmentation method with k-means clustering using a dataset from The Cancer Imaging Archive (TCIA) named SPIEAAPM Lung CT Challenge dataset is performed in this study. The image data is divided into a number of k clusters that are mutually exclusive of each other. In CT images the value of k is considered as 2 as there are 2 regions, one is lung and other is background. This k-means clustering process will be done in two phases, the first phase k clusters will be formed by taking each pixel intensity value to the nearest centroid by calculating the distance between pixel intensity values to each centroid using different distance calculation methods. The segmentation results show that the accuracy, sensitivity is achieved very well to differentiate and pinpoint the clusters.}, pages = {34--45} url = {https://ejournal.undip.ac.id/index.php/berkala_fisika/article/view/60737} }
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