PERHITUNGAN DAN PEMISAHAN SEL DARAH PUTIH BERDASARKAN CENTROID DENGAN MENGGUNAKAN METODE MULTI PASS VOTING DAN K-MEANS PADA CITRA SEL ACUTE LEUKEMIA

Nursanti Novi Arisa, Chastine Fatichah

Abstract


Leukemia is one of the dangerous diseases that can cause death. One of the types of leukemia is acute leukemia that includes ALL (Acute Lymphoblastic Leukemia) and AML (Acute Myeloid Leukemia). The fastest identification against this disease can be done by computing and analysing white blood cell types. However, the manual counting and identification of the white blood cell types are still limited by time. Therefore, automatic counting process is necessary to be conducted in order to get the results more quickly and accurately. Previous studies showed that automatic counting process in the image of Acute Leukemia cells faced some obstacles, the existence of touching cell and the implementation of  geometry feature that cannot produce an accurate counting. It is because the shapes of the cell are various. This study proposed a method for the counting of white blood cells and the separation of touching cells on Acute Leukemia cells image by using Multi Pass Voting method (MPV) based on seed detection (centroid) and K-Means method. Initial segmentation used for separating foreground and background area is canny edge detection. The next stage is seed detection (centroid) using Multi Pass Voting method. The counting of white blood cells is based on the results of the centroid produced. The existence of the touching cells are  separated using K-Means method, the determination of the initial centroid  is based on the results of the Multi Pass Voting method. Based on the evaluation results of 40 images of Acute Leukemia dataset, the proposed method is capable to properly compute based on the centroid. It is also able to separate the touching cell into a single cell. The accuracy of the white blood cell counting result is about 98,6%.


Full Text:

PDF

References


Bhattacharjee, R., Mohan Saini, L.” Robust Technique for the Detection of Acute Lymphoblastic Leukemia”. IEEE. 2015.

Dnyandeo Varsha, S., Nipanikar S. R. “A Review of Adaptive Thresholding Technique for Vehicle Number Plate Recognition”. IJARCCE. 2016.

Effendy, F. Segmentasi Sel Darah Merah Bertumpuk Berdasarkan Fitur Geometri Pada Perhitungan Sel Darah Merah. 2013.

Fathichah, C., Purwitasari D., Hariadi V., Effendy F., “Overlapping White Blood Cell Segmentation and Counting on Microscopic Blood Cell Images”, Int. Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 3., Hal 1271-1286, 2014.

Labati, R. D., Piuri, V., dan Scotti, F. “All-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing”, Proceedings of the 18th IEEE ICIP International Conference on Image Processing, Eds: Macq, B., dan Schelkens, P., IEEE Signal Processing Society, Brussels, hal. 2045-2048. 2013.

Lu, Cheng., Xu, H., Xu, Jun, Gilmore, Hannah, Mandal, Mrinal & Madabhushi, Anant. “Multi Pass Adaptive Voting for Nuclei Detection in Histopathological Images”. Scientific Reports. 2016.

Mandyartha, Eka P., Fatichah Chastine.” Three-level Local Thresholding Berbasis Metode Otsu untuk Segmentasi Leukosit pada Citra Leukemia Limfoblastik Akut". Jurnal Buana Informatika, Volume 7, Nomor 1, Hal: 43-54. 2016.

Nazlibilek, S., Karacor, D., Ercan, T., Sazli, M. H., Kalender, O., dan Ege, Y. “Automatic segmentation, counting, size determination and classification of white blood cells”. Measurement, Vol. 55, Hal. 58–65. 2014.

Piuri, V., dan Scotti, F. “Morphological Classification of Blood Leucocytes by Microscope Images”, Proceedings of the 2004 IEEE International Conference on Countingal Intelligence for Measurement Systems and Applications, Eds: Alippi, C. et al., IEEE, Boston, hal. 103-108. 2004.

Putzu, L., Caocci Giovanni, & Ruberto D. C. “Leucocyte Classification for Leukemia Detection Using Image Processing Techniques”. Sciendirect. 2014.

Xu, H., Lu, Cheng, Mandal, Mrinal. “An Efficient Technique for Nuclei Segmentation based on Ellipse Descriptor Analysis and Improved Seed Detection Algorithm”. IEEE. 2013.




DOI: http://dx.doi.org/10.12962/j24068535.v16i2.a661

Refbacks

  • There are currently no refbacks.


Free counters!
Creative Commons License
JUTI (Jurnal Ilmiah Teknologi Informasi) by Department of Informatics, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. JUTI is accordance with CC BY-SA.