1
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Wang J, Qin L, Chen D, Wang J, Han BW, Zhu Z, Qiao G. An improved Hover-net for nuclear segmentation and classification in histopathology images. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08394-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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2
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Premalatha R, Dhanalakshmi P. Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach. Neural Comput Appl 2022; 34:11553-11569. [PMID: 35250182 PMCID: PMC8889401 DOI: 10.1007/s00521-022-07043-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 01/30/2022] [Indexed: 11/16/2022]
Abstract
Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values.
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3
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Anilkumar KK, Manoj VJ, Sagi TM. Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison. Med Eng Phys 2021; 98:8-19. [PMID: 34848042 DOI: 10.1016/j.medengphy.2021.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/04/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize and classify leukemia. But these methods are costly, slow and affected by the proficiency and expertise of the specialists concerned. Leukemia can be detected with the help of image processing-based methods by analyzing microscopic smear images to detect the presence of leukemic cells and such techniques are simple, fast, cheap and not biased by the specialists. The proposed study presents a computer aided diagnosis system that uses pretrained deep Convolutional Neural Networks (CNNs) for detection of leukemia images against normal images. The use of pretrained networks is comparatively an easy method of applying deep learning for image analysis and the comparison results of the present study can be used to choose appropriate networks for diagnostic tasks. The microscopic images used in the proposed work were downloaded from a public dataset ALL-IDB. In the proposed work, image classification is done without using any image segregation and feature extraction practices and the study used pretrained series network AlexNet, VGG-16, VGG-19, Directed Acyclic Graph (DAG) networks GoogLeNet, Inceptionv3, MobileNet-v2, Xception, DenseNet-201, Inception-ResNet-v2 and residual networks ResNet-18, ResNet-50 and ResNet-101 for performing the classification and comparison. A classification accuracy of 100% is obtained with all the pretrained networks used in the study for ALL_IDB1 dataset and for ALL_IDB2 dataset, 100% accuracy is obtained with all networks except the AlexNet and VGG-16. The efficacy of three optimization algorithms Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square propagation (RMSprop) and Adaptive Moment estimation (ADAM) is also compared in all the classifications performed. The study considered the detection of leukemia in general only, and classification of leukemia into different types can be attempted as a future work.
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Affiliation(s)
- K K Anilkumar
- Department of Electronics and Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Pulincunnu P.O., Alappuzha, Kerala 688504, India.
| | - V J Manoj
- Department of Electronics and Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Pulincunnu P.O., Alappuzha, Kerala 688504, India
| | - T M Sagi
- Department of Medical Lab Technology, St. Thomas College of Allied Health Sciences, Changanacherry P.O., Kottayam, Kerala 686104, India
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4
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Ghaderzadeh M, Aria M, Hosseini A, Asadi F, Bashash D, Abolghasemi H. A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images. INT J INTELL SYST 2021. [DOI: 10.1002/int.22753] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Mustafa Ghaderzadeh
- Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Mehrad Aria
- Department of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran
| | - Hassan Abolghasemi
- Pediatric Congenital Hematologic Disorders Research Department Shahid Beheshti University of Medical Sciences Tehran Iran
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5
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Baby D, Devaraj SJ, Anishin Raj MM. Leukocyte classification based on statistical measures of radon transform for monitoring health condition. Biomed Phys Eng Express 2021; 7. [PMID: 34624876 DOI: 10.1088/2057-1976/ac2e16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/08/2021] [Indexed: 11/12/2022]
Abstract
In the medical field, automated and computerised analytic tools are essential for faster disease diagnosis. The main objective of this research work is to classify the leukocytes accurately into four different subtypes based on the pattern of the nucleus. The features are extracted from the segmented nucleus, which play a vital role in the pattern recognition. The technique comprises a novel idea of computing the statistical measures such as peak difference and standard deviation of the radon transformed graph for a single angle of rotation along with other features. Three Gray Level Co-occurrence Matrix (GLCM) based features, two geometric features and four RST moment invariants are also extracted for feature fusion. The fused feature vectors are trained and evaluated using random forest classification algorithm.This method provides an overall accuracy of 97.61% and it is able to determine the lymphocyte, neutrophil and eosinophil with 100% accuracy. The classification without incorporating radon transform features is also performed which provides an accuracy of only 80.95%.
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Affiliation(s)
- Diana Baby
- Research Scholar, Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - Sujitha Juliet Devaraj
- Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - M M Anishin Raj
- Department of CSE, Viswajyothi College of Engineering and Technology, Kerala, India
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6
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Wang C, Zhang H, Li Z, Zhou X, Cheng Y, Chen R. White Blood Cell Image Segmentation Based on Color Component Combination and Contour Fitting. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017102310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
White Blood Cell (WBC) image segmentation plays a key role in cell
morphology analysis. However, WBC segmentation is still a challenging task due to the diversity
of WBCs under different staining conditions.
Objective:
In this paper, we propose a novel WBC segmentation method based on color component
combination and contour fitting to segment WBC images accurately.
Methods:
Specifically, the proposed method first uses color component combination and image
thresholding to achieve nucleus segmentation, then uses a color prior to remove image background,
and extracts the initial WBC contour via Canny edge detection, and finally judges and
closes the unclosed WBC contour by contour fitting. Accordingly, cytoplasm segmentation is
achieved by subtracting the nucleus region from the WBC region.
Results:
Experimental results on 100 WBC images under rapid staining condition and 50 WBC
images under standard staining condition showed that the proposed method improved segmentation
accuracy of white blood cells under rapid and standard staining conditions.
Conclusion:
The proposed color component combination and contour fitting is effective in WBC
segmentation task.
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Affiliation(s)
- Chuansheng Wang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Hong Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
| | - Xiaogen Zhou
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
| | - Yong Cheng
- School of Information Mechanical & Electrical Engineering, Jiangsu Open University, Nanjing, China
| | - Rongyan Chen
- Department of Clinical Laboratory, the People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, China
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7
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Anilkumar K, Manoj V, Sagi T. A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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8
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Wang S, Liang S, Peng F. Image edge detection algorithm based on fuzzy set. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179578] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Shuqiang Wang
- School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
- School of Information and Electricity Engineering, Hebei University of Engineering, Handan, China
| | - Shuo Liang
- School of Information Engineering, Handan College, Handan, China
| | - Fei Peng
- School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
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9
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Hegde RB, Prasad K, Hebbar H, Singh BMK, Sandhya I. Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images. J Digit Imaging 2019; 33:361-374. [PMID: 31728805 DOI: 10.1007/s10278-019-00288-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.
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Affiliation(s)
- Roopa B Hegde
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India. .,Department of ECE, NMAM Institute of Technology (Visvesvaraya Technological Univerity, Belagavi), Nitte, Karnataka, 574110, India.
| | - Keerthana Prasad
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India
| | | | - Brij Mohan Kumar Singh
- Department of Immunohematology and Blood Transfusion KMC, MAHE, Manipal, Karnataka, 576104, India
| | - I Sandhya
- Department of Pathology, A J Institute of Medical Sciences and Research Center, Kuntikana, Mangalore, 575004, India
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10
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Wang Y, Cao Y. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation. Med Phys 2019; 47:142-151. [PMID: 31691975 DOI: 10.1002/mp.13904] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 09/12/2019] [Accepted: 10/31/2019] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Human peripheral blood leukocytes' classification is important for diagnosing blood diseases. Many microscopic leukocyte image automatic detection methods are proposed. In recent years, convolutional neural networks (CNNs) are applied to microscopic leukocyte image automatic classification. But when a CNN is used for microscopic leukocyte image classification, the dataset's scarcity and imbalance will lead to low classification accuracy. To improve classification accuracy, a data augmentation method is proposed, and a resampling method is adopted when using a CNN method. METHODS First, a deep CNN model for microscopic leukocyte image classification is designed. Then, a new data augmentation method based on feature concentration is proposed to enrich the dataset and overcome the problem of dataset scarcity. To make the CNN model focus on the leukocyte region, many images are generated by putting a segmented leukocyte into images with different microscopic surroundings using an image processing method. Finally, taking the imbalance of the five kinds of leukocytes in the dataset into consideration, a resampling method is adopted. The resampling method iteratively feeds the leukocyte images with a low proportion to the CNN model within an epoch to ensure that images of each of the five kinds of leukocytes are represented in relatively equal numbers in each batch. RESULTS The experimental results demonstrate that the proposed classification method can achieve 97.6% average testing accuracy. Classification precision for the five kinds of leukocytes is above 93.4%, while sensitivity is above 92.5%. Both the proposed data augmentation and the resampling methods improve classification accuracy. CONCLUSIONS A human peripheral blood leukocyte classification method based on a CNN and data augmentation is proposed. The problem of dataset scarcity is solved by the proposed data augmentation method, and the dataset imbalance is solved by a resampling method.
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Affiliation(s)
- Yapin Wang
- Department of Opto-electronics, SichuanUniversity, Chengdu, 610064, China
| | - Yiping Cao
- Department of Opto-electronics, SichuanUniversity, Chengdu, 610064, China
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11
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Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis. J Med Syst 2019; 43:82. [PMID: 30798374 DOI: 10.1007/s10916-019-1185-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/30/2019] [Indexed: 10/27/2022]
Abstract
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu's method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods.
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12
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Qin P, Zhang J, Zeng J, Liu H, Cui Y. A framework combining DNN and level-set method to segment brain tumor in multi-modalities MR image. Soft comput 2019. [DOI: 10.1007/s00500-019-03778-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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13
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Zhang J, Zhong Y, Wang X, Ni G, Du X, Liu J, Liu L, Liu Y. Computerized detection of leukocytes in microscopic leukorrhea images. Med Phys 2017; 44:4620-4629. [DOI: 10.1002/mp.12381] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/15/2017] [Accepted: 05/19/2017] [Indexed: 11/06/2022] Open
Affiliation(s)
- Jing Zhang
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Ya Zhong
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Xiangzhou Wang
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Guangming Ni
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Xiaohui Du
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Juanxiu Liu
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Lin Liu
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
| | - Yong Liu
- School of Optoelectronic Information; University of Electronic Science and Technology of China; Chengdu 610054 China
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14
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Liu Y, Cao F, Zhao J, Chu J. Segmentation of White Blood Cells Image Using Adaptive Location and Iteration. IEEE J Biomed Health Inform 2016; 21:1644-1655. [PMID: 27834657 DOI: 10.1109/jbhi.2016.2623421] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the GrabCut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.
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