Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images

Abstract

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

“…The work presents a robust image processing algorithm for detection of nuclei and classification of WBCs based on features of the nuclei. Additionally, used a novel image enhancement method to manage illumination variations and tissue quant method to manage color variations for the detection of nuclei (Hegde, Prasad, Hebbar, & Singh, ; Saba, Rehman, et al, 2018).…”

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

“…To design and develop ML algorithms, hematologists have made some of these datasets (that include PBS images) available to researchers. ALL-IDB, one of the most well-known datasets published in two versions, has been utilized in many articles, most of which have diagnosed and classified acute lymphoblastic leukemia (ALL) via different ML techniques [16][17][18][19][20][21]. ere is another published leukemia dataset called Benchmark for the development of ML algorithms, used by some studies.…”

“…Al-jaboriy et al used the nuclear-to-cytoplasmic ratio, nucleus compactness, nucleus form factors, nucleus eccentricity, nucleus elongation, and nucleus rigidity [17,23,24,27]. Among seven studies, which used traditional ML algorithm, four used the SVM method alone and with other algorithms [18][19][20]24] and three utilized ANN and other algorithms [17]. Note that these algorithms are among the most popular algorithms in medical image processing.…”

Raabin-WBC: a large free access dataset of white blood cells from normal peripheral blood

“…As you can see in table 1, the characteristics of nucleus and cytoplasm can significantly affect determining the type of the cell. Some papers [36, 16] extract different features from the nucleus and the cytoplasm to classify white blood cells. These features usually describe the shape and the color of the nucleus and the cytoplasm.…”