Blood cancer is one of the types of cancer. Leukemia is one among them. Which was caused due to the abnormal growth of the white blood cells in the bone marrow in the blood. This also affects the functionality of the white blood cells and the red blood cells, platelets. The leukaemia is further divided into four types. In this paper, we are going to discuss and examine the results of the acute lymphoblastic leukemia. In the phase of segmentation procedure, we have taken the edge-based segmentation. Here we will see the results by applying the median filter once and twice with different masks along with different operators. For the process, we have done it in processing tool like Pycharm with python, Opencv package. We have observed the difference between the operators with its functionality, changing the mask values for filtration. The good segmentation leads to the accuracy in classification.

Blood cancer, ALL (Acute Lymphoblastic Leukemia), Image Segmentation, Pycharm

[1] Pooja Deshmukh, prof. C. R. Jadhav, ”A Survey on Detection of Blood Cancer Using White Blood Cells Segmentation”, International Journal of Modern Trends in Engineering And Research(IJMTER), volume02, Issue12, ISSN (print):2393-8161.
[2] Subrajeet Mohapatra, Dipi Patra, Sanghamitra Satpathy, “Unsupervised Blood Microscopic Image Segmentation and Leukemia Detection Using Color Based Clustering”, International journal of Computer information system and industrial Management Application, Vol 4, 2012, pp. 477-485.
[3] Vasundhara Sharma, Sonu Elsa Jacob, Praveen Kumar Sharma, “A Comparative Study of Techniques For Leukemia Detection”, International Journal of Advance Research in Science and Engineering IJARSE, vol.No.4, Special Issue (01), March2015, ISSN-2319-8354(E).
[4] T. Karthikeyan, N. Poornima, Microscopic image segmentation using fuzzy c means for leukemia diagnosis, Leukemia 4(1).
[5] Tejashri G. Patil, V.B. Raskar, “Blood microscopic Image Segmentation & acute leukemia detection” (IJERMT) ISSN: 2278-9359(volume-4, Issue-9).
[6] Manisha, Dr. T. Sethukarasi “Automated Detection of White Blood Cell Cancer Disease Using Classification Techniques-A Survey”, (IRJET), Vol.No:06, Issue 02, FEB 2019. E-ISSN: 2395-0056.
[7] Carolina Reta, Leopoldo Altamirano, Jesus A. Gonzalez, Ranquel Diaz and Jose S. Guichard (2015), “segmentation of bone marrow cells images for morphological classification of acute leukemia”. Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010).
[8] Rejintal, A., Aswini, N. 2016. “Image processing based leukemia cancer cell detection”. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pages 471–474.
[9] Meghana M.R, Akshatha Prabhu “An Efficient Technique for Identification of Leukemia in Microscopic Blood Samples Using Image Processing”,(IJRPS),ISSN:
0975-7538, year-2019.
[10] Nimesh Patel, Ashutosh Mishra “Automated Leukemia Detection Using Microscopic Images”, procedia computer science 58(2015)635-642.
[11] Shiv Kumar chatarwad, Pratik Bansode, Amar Burade, prof. T.S Chaware, “Blood Cancer Detection Using Image Processing”, (IJARECE)vol.no:7,Issue:5
May 2018, ISSN: 2278-909X.
[12] Chaitali Raje, Jyoti Rangole “Detection of Leukemia in Microscopic Image Using Image Processing”, conference paper. April 2014, ICCSP.2014.6949840.