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IJ Radiology

An Innovative Journal in the Field of Radiology

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https://doi.org/10.5812/iranjradiol.99159

Automatic Detection and Classification of White Blood Cells in Blood Smear Images Using Convolutional Neural Network

Author(s):
Ramin NateghiRamin Nateghi1,*, Mansoor FatehiMansoor Fatehi2, Ali SadeghitabarAli Sadeghitabar3, Romana KhosraviRomana Khosravi3, Fattane PourakpourFattane Pourakpour2
1Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran
2National Brain Mapping Laboratory, Tehran, Iran
3Avicenna Fertility Center, Tehran, Iran
IJ Radiology:Vol. 16, issue Special Issue; e99159
Published online:Dec 10, 2019
Article type:Abstract
Received:Oct 26, 2019
Accepted:Dec 10, 2019
How to Cite:Nateghi R, Fatehi M, Sadeghitabar A, Khosravi R, Pourakpour F. Automatic Detection and Classification of White Blood Cells in Blood Smear Images Using Convolutional Neural Network. I J Radiol. 2019;16(Special Issue):e99159. doi: https://doi.org/10.5812/iranjradiol.99159

Abstract

Background:

Blood cell identification and counting are very important in the diagnosis and treatment of diseases. Of the blood cells, the identification of white blood cells (WBC) and their changes is of particular importance due to their role in the immune system. Manual cell counting is time-consuming and dependent on expert experience. Also, the accuracy of blood cell counting can be influenced by human limitations such as fatigue and mental problems. Automatic systems can be a convenient and cost-effective choice for routine clinical services and can be used for fast and accurate blood disease diagnosis. In the automated systems, blood samples are analyzed using microscopic images of stained blood cells. There are various studies on automatic blood cell segmentation based on blood smear images [1-4]. Also, some studies have focused on WBC image classification [5-6].

Objectives:

In this paper, the main objective is to provide the implementation of a deep learning-based automatic system to identify five main groups of WBCs in human peripheral blood smear, including Eosinophils, Basophils, Monocytes, Lymphocytes, and Neutrophils.

Method:

The block diagram of the proposed method is shown in Figure 1. As can be seen, the proposed method consisted of three pre-processing, segmentation, and deep learning-based classification stages. In the pre-processing stage, color normalization was used to normalize the color appearance variability. The color appearance can significantly vary between different labs due to differences in slide digitization conditions and staining protocols. Automatic image analysis methods can be significantly affected by different smear color appearances. In the color normalization stage, images to be examined were normalized to match the color appearance of a target image with standard calibrated staining. The second task was the removal stage of the background and segmentation of the desired region of WBCs. In this stage, by subtracting the B color channel of RGB blood smear image from G color channel and then by using morphological erosion and morphological reconstruction, the WBC probability map was obtained. The value of the WBC probability map showed that the pixels how likely were related to WBCs. In WBC probability map image, the pixels belonging to the WBCs had larger values than the pixels of non-WBCs. Finally, WBCs were segmented by applying optimal Otsu’s thresholding [7] on the probability map image. Detected WBCs were cropped from entire image by considering a patch with size 131 × 131 around all detected cells. The patches for segmented WBCs were then passed through a convolutional neural network (CNN) called CellDiff-Net, which returned the class of WBCs. The structure of the CNN architecture is shown in Figure 2.

Results:

Our blood smear image database used to train the proposed method was composed of 216 images with size 1536 × 2048. They were collected and labeled by experts at Avicenna Infertility Clinic (ACECR), Tehran, Iran. Stepwise processing of a sample blood smear image for WBC segmentation is shown in Figure 3. By the approach shown in Figure 3, all WBC patches were extracted from training images. Image augmentation (flips, rotations, and shears) was used to increase the size of the training set and balance out the classes. We tested our model for a test set of 10 blood smear samples. Then, 100 images were captured from each blood sample and all images were analyzed by the proposed method. Visualizing feature space in convolution layers for a test WBC image pass through learned CellDiff-Net is shown in Figure 4. To evaluate cell differential counts, the performance of the proposed method was compared with the results of manual counting and Sysmex kx-21 analyzer. Figure 5 compares three automated, Sysmex, and manual differential cell count results for a test sample. For objective evaluation of the proposed system, three criteria of sensitivity, specificity, and accuracy were used. The manually labeled WBCs were considered as ground-truth. The ground truth for all the images was determined by an expert and used to validate the proposed method. Table 1 shows the performance of the automated proposed WBC detection and classification method.

Conclusion:

In this paper, a novel automated system was proposed for WBC detection and classification in blood smear images. The experimental results proved the performance of the proposed system in WBC detection and classification.

Keywords
White Blood Cell Classification
Image Classification
Blood Smear Images

To see figures, table, and references, please refer to the PDF file.

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