Computer Aided System for Red Blood Cell Classification in Blood Smear Image

Tomari, Razali; Zakaria, Wan Nurshazwani Wan; Jamil, M. Mahadi Abdul; Nor, Faridah Mohd; Fuad, Nik Farhan Nik

doi:10.1016/j.procs.2014.11.053

Cited by 84 publications

(46 citation statements)

References 10 publications

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“…In our previous work, RBC classification in blood smear image was conducted [7]. However, the work only classified the normal, abnormal and overlapping cells without separating the agglomerates RBC.…”

Section: Introductionmentioning

confidence: 99%

Red blood cell image enhancement techniques for cells with overlapping condition

Oussama

Jamil

Hafizah

et al. 2018

J. Fundam and Appl Sci.

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This paper proposes an automatic algorithm consists of techniques that can separate overlapping RBC for enhancing RBC image to improve counting precision. The proposed algorithm is able to automatically count RBC of colour blood smear images based on the quality or challenging conditions of the images such as poor illumination of blood smear and most importantly overlapping RBC. The algorithm comprises of two RBC segmentation that can be selected based on the image quality, circle mask technique and grayscale blood smear image processing. Detail explanations including experimental results that show the effectiveness of the proposed techniques are described in this paper. The proposed algorithm has been successfully detect and separate agglomerate RBC with 96% accuracy. Algorithm validation was verified with RBC distribution area and confusion matrix.

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Section: Introductionmentioning

confidence: 99%

Red blood cell image enhancement techniques for cells with overlapping condition

Oussama

Jamil

Hafizah

et al. 2018

J. Fundam and Appl Sci.

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“…One way to analyze a blood smear is to use image processing techniques [5][6][7][8]. Image processing is one of the areas of data mining and method for extracting additional information about processes under experimental study.…”

Section: Introductionmentioning

confidence: 99%

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Babker

Lyashenko

2018

IJCBR

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Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure.

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“…ANN is one of the commonly used classification techniques. Tomari et al (2014) have used ANN for classification of red blood cells into normal and abnormal once the segmented cells are obtained. Similar work has been proposed in Lee & Chen (2014) with difference being the kind of features selected and used for classification.…”

Section: Introductionmentioning

confidence: 99%

MRF‐ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images

Mungle

Tewary

Das

et al. 2017

Journal of Microscopy

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Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells. The proposed segmentation methodology is found to have F-measure 0.95. Artificial neural network is subsequently used to obtain intensity-based score for ER cells, from pixel colour intensity features. Simultaneously, proportion score - percentage of ER positive cells is computed via cell counting. The final ER score is computed by adding intensity and proportion scores - a standard Allred scoring system followed by pathologists. The classification accuracy for classification of cells by classifier in terms of F-measure is 0.9626. The problem of subjective interobserver ability is addressed by quantifying ER score from two expert pathologist and proposed methodology. The intraclass correlation achieved is greater than 0.90. The study has potential advantage of assisting pathologist in decision making over manual procedure and could evolve as a part of automated decision support system with other receptor scoring/analysis procedure.

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Computer Aided System for Red Blood Cell Classification in Blood Smear Image

Cited by 84 publications

References 10 publications

Red blood cell image enhancement techniques for cells with overlapping condition

Red blood cell image enhancement techniques for cells with overlapping condition

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

MRF‐ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images

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