Leukocyte segmentation and counting based on microscopic blood images using HSV saturation component with blob analysis

Quinones, Vonn Vincent; Macawile, Merl James P.; Bailado, Alejandro; Cruz, Jennifer C. Dela; Caya, Meo Vincent C.

doi:10.1109/iccre.2018.8376475

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…A threshold method was also proposed by other researchers. [8][9][10][11][12] recommended a watershed-based and Otsu threshold-based segmentation which resulted in 99.3% and 93.3% accuracy, respectively. K-means clustering was another famous segmentation approach.…”

Section: Literature Reviewmentioning

confidence: 99%

WBC-based segmentation and classification on microscopic images: a minor improvement

Lam

Yoong

et al. 2021

F1000Res

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Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method. Methods A triple thresholding method was proposed to segment the WBCs; meanwhile, a convolutional neural network (CNN)-based binary classification model that adopts transfer learning technique was proposed to detect and classify WBCs as a healthy or a malignant. The input dataset of this research work is the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). The process first converts the captured microscope images into HSV format for obtaining the H component. Otsu thresholding is applied to segment the WBC area. A 13 × 13 kernel with two iterations was used to apply morphological opening on image to ameliorate output results. Collected cell masks were used to detect the contour of each cell on the original image. To classify WBCs into a healthy or a malignant category, characteristics and conditions of WBCs are to be examined. A transfer learning technique and pre-trained InceptionV3 model were employed to extract the features from the images for classification. Results The proposed WBCs segmentation method yields 90.45% accuracy, 83.81% of the structural similarity index, 76.25% of the dice similarity coefficient, and is computationally efficient. The accuracy of fine-tuned classifier model for training, validation and test sets are 93.27%, 92.31% and 96.15% respectively. The obtained results are high in accuracy and precision are over 96% and with lower loss value. Discussion Triple thresholding outperforms K-means clustering in segmenting smaller dataset. Pre-trained InceptionV3 model and transfer learning improve the flexibility and ability of classifier.

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Section: Literature Reviewmentioning

confidence: 99%

WBC-based segmentation and classification on microscopic images: a minor improvement

Lam

Yoong

et al. 2021

F1000Res

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“…But most of the work discusses the treatment of single cell from the image (WBCs or RBCs). In [14,15], Quiñones et al and Shahin et al developed an algorithm for the counting and segmentation of leukocytes (WBCs) by using HSV color space/Zak algorithm and adaptive neutrosophic similarity score/Otsu's thresholding, respectively. In [15], BS_DB3 and ALL_DB1 and ALL_DB2 [12] datasets were used for segmentation purposes, while Quiñoneset al [14] used a total of 12 blood smear images for counting specific type of blood cells in an image.…”

Section: Related Workmentioning

confidence: 99%

“…In [14,15], Quiñones et al and Shahin et al developed an algorithm for the counting and segmentation of leukocytes (WBCs) by using HSV color space/Zak algorithm and adaptive neutrosophic similarity score/Otsu's thresholding, respectively. In [15], BS_DB3 and ALL_DB1 and ALL_DB2 [12] datasets were used for segmentation purposes, while Quiñoneset al [14] used a total of 12 blood smear images for counting specific type of blood cells in an image. Liu et al [16] performed segmentation of white blood cells using mean shift clustering and watershed operation on 306 images collected from Hospital of Shandong University.…”

Section: Related Workmentioning

confidence: 99%

“…is technique only outperforms on WBCs not for RBCs and platelets. In [14,15], Quiñones et al and Shahin et al developed an algorithm for the counting and segmentation of leukocytes (WBCs) by using HSV color space/Zack algorithm and adaptive neutrosophic similarity score/Otsu's thresholding, respectively. Yi et al [22] segmented only RBCs with the help of the markercontrolled watershed transform algorithm on manually collected 117 images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

Shahzad

Umar

Khan

et al. 2020

Computational and Mathematical Methods in Medicine

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Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

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“…In the work proposed by Quiñones et al [27] in "Leukocyte Segmentation and Counting based on Microscopic Blood Images using HSV Saturation Component with Blob Analysis", they eliminated the need for morphological operations such as area opening, dilation and erosion thoroughly in their algorithm. The original RGB image is converted into HSV color space so that the S component that predominantly shows the contrast the leukocytes in the grayscale image.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Efficient Implementation of Nucleus Detection and Segmentation Using Correlated Dual Color Space

Khong

Mirzaei

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Leukocyte segmentation and counting based on microscopic blood images using HSV saturation component with blob analysis

Cited by 17 publications

References 7 publications

WBC-based segmentation and classification on microscopic images: a minor improvement

WBC-based segmentation and classification on microscopic images: a minor improvement

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

A Novel Approach for Efficient Implementation of Nucleus Detection and Segmentation Using Correlated Dual Color Space

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