CLASSIFICATION OF WHITE BLOOD CELLS BASED ON SURF FEATURE

被引:0
作者
Noor, Anas Mohd [1 ,2 ]
Zakaria, Zulkarnay [1 ,2 ]
Noor, Aishah Mohd [3 ]
Norah, Ahmad Nasrul [1 ,2 ]
机构
[1] Univ Malaysia Perlis, Sch Mechatron Engn, Bioelect Instrumentat Res Grp, Arau, Perlis, Malaysia
[2] Univ Malaysia Perlis, Sch Mechatron Engn, Biomed Elect Engn, Arau, Perlis, Malaysia
[3] Univ Malaysia Perlis, Inst Engn Math, Arau, Perlis, Malaysia
来源
SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY | 2021年 / 28卷 / 01期
关键词
ANN; SURF; SVM; Blood smear; WBCs classification; SEGMENTATION;
D O I
暂无
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Conventional blood analysis using blood smear image were performed manually by experts in hematology is tedious and highly depending on the level of experience. Currently, computer-assist technology is developed to reduce the time-consuming process and improved accuracy. As an example, various image processing techniques used to quantify such as white blood cells (WBCs) morphological conditions or classification in the blood smear image, which assist experts in developing confidence decision making in the analysis of cells conditions linked to the specific diseases. However, the WBCs shape features are arbitrary than the red blood cells (RBCs) because of the maturation state, cell orientations or positions, cell color variations, and the quality of the image captured influences the performance of classification accuracy. Therefore, we proposed a scale and rotation invariance feature for WBCs classification using speed up robust feature (SURF). SURF is suitable to be applied in identifying objects even though the orientation, scale, and position are varying, such as WBCs in microscopic blood smear images. We analyzed the classification performances using a support vector machine (SVM) and an artificial neural network (ANN) of WBCs types in the microscopic image based on the cell nucleus. The results show that the purposed SURF feature method has an excellent performance of accuracy for both methods and suitable to be utilized for the application of cell types classification.
引用
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页数:6
相关论文
共 19 条
[1]  
Adewoyin A S, 2014, Ann Ib Postgrad Med, V12, P71
[2]  
AL-Dulaimi K, 2018, 2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), P19
[3]  
[Anonymous], 2013, Int J Innovat Res
[4]  
[Anonymous], 2018, P WORKSH VIS COMP IL
[5]   SURF: Speeded up robust features [J].
Bay, Herbert ;
Tuytelaars, Tinne ;
Van Gool, Luc .
COMPUTER VISION - ECCV 2006 , PT 1, PROCEEDINGS, 2006, 3951 :404-417
[6]   Microscopic image segmentation based on pixel classification and dimensionality reduction [J].
Benazzouz, Mourtada ;
Baghli, Ismahan ;
Chikh, Med Amine .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2013, 23 (01) :22-28
[7]   White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks [J].
Choi, Jin Woo ;
Ku, Yunseo ;
Yoo, Byeong Wook ;
Kim, Jung-Ah ;
Lee, Dong Soon ;
Chai, Young Jun ;
Kong, Hyoun-Joong ;
Kim, Hee Chan .
PLOS ONE, 2017, 12 (12)
[8]  
Chris Lina Arlends, 2016, International Journal of Computer Theory and Engineering, V8, P69, DOI 10.7763/IJCTE.2016.V8.1022
[9]  
Hiremath P., 2010, International Journal of Computer Applications, Special Issue on RTIPPR, P59
[10]  
천민규, 2011, International Journal of Fuzzy Logic and Intelligent systems, V11, P293