Breast Cancer Image Segmentation Using K-Means Clustering Based on GPU Cuda Parallel Computing

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Andika Elok Amalia
Gregorius Airlangga
Afandi Nur Aziz Thohari


Image processing technology is now widely used in the health area, one example is to help the radiologist to analyze the result of MRI (Magnetic Resonance Imaging), CT Scan and Mammography. Image segmentation is a process which is intended to obtain the objects contained in the image by dividing the image into several areas that have similarity attributes on an object with the aim of facilitating the analysis process. The increasing amount  of patient data and larger image size are new challenges in segmentation process to use time efficiently while still keeping the process quality. Research on the segmentation of medical images have been done but still few that combine with parallel computing. In this research, K-Means clustering on the image of mammography result is implemented using two-way computation which are serial and parallel. The result shows that parallel computing  gives faster average performance execution up to twofold.


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How to Cite
A. E. Amalia, G. Airlangga, and A. N. A. Thohari, “Breast Cancer Image Segmentation Using K-Means Clustering Based on GPU Cuda Parallel Computing”, INFOTEL, vol. 10, no. 1, pp. 33-38, Feb. 2018.


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