A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-Knn Classifier

Even in this modern era today, life's extent is still being challenged by many pathological diseases such as cancer. One such hazard is leukemia. Even a trivial setback in detecting leukemia lead to a severe outcome: the affected cells may eventually prove to be fatal. To combat this, we propose an algorithm to better segment the nucleus region of White Blood Cells (WBC) found in stained blood smear images with the intent of identifying Acute Lymphoblastic Leukemia (ALL). In our proposal, the image is made ready for segmentation in the preprocessing stage by changing its size, brightness, and contrast. In the segmentation stage, the nucleus region is segmented by mathematical operators and Otsu's thresholding. Then mathematical morphological operators are applied in post-processing stage, which makes the nucleus region convenient for feature extraction. Finally, the segmented regions are classified into ALL affected and regular cells by means of the proposed Customized K-Nearest Neighbor classifier algorithm. This work was experimented with over 80 images of the ALL-IDB2 dataset and attained an accuracy rate of 96.25%, 95% of sensitivity and 97% of specificity.

[1]  Sumita Mishra,et al.  Automated Detection of Acute Leukemia using K-mean Clustering Algorithm , 2016, ArXiv.

[2]  Mohan M. Trivedi,et al.  Object detection based on gray level cooccurrence , 1984, Comput. Vis. Graph. Image Process..

[3]  Vincenzo Piuri,et al.  All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  San Chi Liu,et al.  Image contrast enhancement using histogram equalization with maximum intensity coverage , 2016 .

[5]  C. Pui.  Acute lymphoblastic leukemia. , 1997, Pediatric clinics of North America.

[6]  F. Albregtsen.  Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .

[7]  Saeed Kermani,et al.  Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier , 2015, Journal of medical signals and sensors.

[8]  H. S. Bhadauria,et al.  Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images , 2015 .

[9]  Chih-Fong Tsai,et al.  The distance function effect on k-nearest neighbor classification for medical datasets , 2016, SpringerPlus.

[10]  Dong-Chen He,et al.  Texture feature extraction , 1987, Pattern Recognit. Lett..

[11]  et al. Bhukya.  Detection of acute lymphoblastic leukemia using microscopic images of blood , 2017 .

[12]  Sung Wook Baik,et al.  Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities , 2017, IEEE Access.

[13]  Jitendra Virmani,et al.  Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers , 2017, Multimedia Tools and Applications.

[14]  Mohammad Sadegh Helfroush,et al.  An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images , 2018, Journal of Digital Imaging.

[15]  Atul H. Karode,et al.  White Blood Cells Segmentation and Classification to Detect Acute Leukemia Ms , 2013 .

[16]  Dipti Patra,et al.  Automated morphometric classification of acute lymphoblastic leukaemia in blood microscopic images using an ensemble of classifiers , 2016, Comput. methods Biomech. Biomed. Eng. Imaging Vis..