Main Article Content
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 amountÂ 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 computingÂ gives faster average performance execution up to twofold.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
 Anami, B. S. & Unki, P. H., . “A Combined Fuzzy and Level Sets Based Approach for Brain MRI Image Segmentation.” Jodhpur, IEEE.2013
 Anitha, J. & Peter, D. D,. “A Spatial Fuzzy based Level Set Method for Mammogram Mass Segmentation”. s.l., IEEE.2015
 Erik Smistad et al. “Medical Image Segmentation on GPUs - A Comprehensive Review”. Medical Image Analysis, Volume 20, pp. 1-18.2015
 Aparajeeta, J., Nanda, P. K. and Das, N,. “Modified Possibilistic Fuzzy C-Means Algorithms for Segmentation of Magnetic Resonance Imaging”. Applied Soft Computing, Volume 41, pp. 104-119. 2016
 Banerjee, S., Mitra, S. and Shankar, B. U.,. “Single Seed Delineation of Brain Tumor using multi Thresholding”. Information Sciences. 2015
 Barles, G., Soner, H. M. and Sougandis, P. E,. “Front Propagation and Phase Field Theory”. SIAMJ. Control Optimization, 31(2), pp. 439-469.1993
 Kirk, D. B. & Hwu, W.-M. W,. “Programming Massively Parallel Processors”. Burlington: Elsevier. 2010
 Soesanti, Indah, Adhi Susanto, Thomas Sri Widodo and Maesadji Tjokronegoro “Analisis Komputasi pada Segmentasi Citra Medis Adaptif Berbasis Logika Fuzzy Teroptimasi. Forum Teknik Vol. 33, No. 2.2010”
 Basyid, Fakhrurrozi and Kusworo Adi. “Segmentasi Citra Medis Untuk Pengenalan Objek Kanker Menggunakan Metode Active Contour”. Youngster Physics Journal, Vol. 3, No. 3, pp. 209-216. 2014
 Koprawi, Muhammad , Teguh Bharata Adji dan Dani Adhipta. “Analisis Performa Komputasi Paralel GPU Menggunakan PYCUDA dan PYOPENCL dengan Komputasi Serial CPU pada Citra Digital”. CITEE. 2017
 Utami, Diana Budhi and Muhammad Ichwan. “Pengenalan Pose Tangan Menggunakan HuMoment”. Jurnal Infotel. Vol.9 No.1 Februari 2017
 Badawy, Samir M, Alaa A. Hefnawy, Hassan E. Zidan and Mohammed T. GadAllah. “Breast Cancer Detection with Mammogram Segmentation : A Qualitative Study”. International Journal of Advanced Computer Science and Application, Vol 8, No 10. 2017
 J Suckling et al. "MIAS MiniMammographic Database",Mammographic Image Analysis Society (MIAS). 19 January 1995. Available URL: http://peipa.essex.ac.uk/info/mias.html
 Blaise Barney. “What is Parallel Computing ?”. [online] 2009. Available URL : https://computing.llnl.gov/tutorials/parallel_comp/
 NVIDIA CUDA C Programming Guide. NVIDIA Corporation. 2012
 Suyanto. “Data Mining Untuk Klasifikasi dan Klasterisasi Data”. Bandung : Penerbit Informatika. 2017
 J. A. Hartigan and M. A. Wong . “A K-Means Clustering Algorithm”. Applied Statistics, Vol. 28, No. 1, p100-108. 1979