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Link A, Pardo IL, Porr B, Franke T. AI based image analysis of red blood cells in oscillating microchannels. RSC Adv 2023; 13:28576-28582. [PMID: 37780736 PMCID: PMC10537593 DOI: 10.1039/d3ra04644c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
The flow dynamics of red blood cells in vivo in blood capillaries and in vitro in microfluidic channels is complex. Cells can obtain different shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (RBCs) in an oscillating microchannel to distinguish healthy red blood cells from red blood cells treated with formaldehyde to chemically modify their viscoelastic behavior. We used TensorFlow to train and validate a deep learning model and achieved a testing accuracy of over 97%. This method is a first step to a non-invasive, label-free characterization of diseased red blood cells and will be useful for diagnostic purposes in haematology labs. This method provides quantitative data on the number of affected cells based on single cell classification.
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Affiliation(s)
- Andreas Link
- Division of Biomedical Engineering, School of Engineering, University of Glasgow Oakfield Avenue G12 8LT Glasgow UK
| | - Irene Luna Pardo
- Division of Biomedical Engineering, School of Engineering, University of Glasgow Oakfield Avenue G12 8LT Glasgow UK
| | - Bernd Porr
- Division of Biomedical Engineering, School of Engineering, University of Glasgow Oakfield Avenue G12 8LT Glasgow UK
| | - Thomas Franke
- Division of Biomedical Engineering, School of Engineering, University of Glasgow Oakfield Avenue G12 8LT Glasgow UK
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K.T. N, Prasad K, Singh BMK. Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review. Med Biol Eng Comput 2022; 60:2445-2462. [PMID: 35838854 PMCID: PMC9365735 DOI: 10.1007/s11517-022-02614-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 04/22/2022] [Indexed: 11/10/2022]
Abstract
Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444–454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world’s second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations.
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Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3626726. [PMID: 35449742 PMCID: PMC9017520 DOI: 10.1155/2022/3626726] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/26/2022] [Indexed: 11/18/2022]
Abstract
Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.
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Marques G, Ferreras A, de la Torre-Diez I. An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:28061-28078. [PMID: 35368860 PMCID: PMC8964254 DOI: 10.1007/s11042-022-12624-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/03/2021] [Accepted: 02/09/2022] [Indexed: 05/05/2023]
Abstract
UNLABELLED Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11042-022-12624-6.
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Affiliation(s)
- Gonçalo Marques
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Antonio Ferreras
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Isabel de la Torre-Diez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
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Petrović N, Moyà-Alcover G, Jaume-i-Capó A, González-Hidalgo M. Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images. Comput Biol Med 2020; 126:104027. [DOI: 10.1016/j.compbiomed.2020.104027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/13/2020] [Accepted: 09/30/2020] [Indexed: 12/01/2022]
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6
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Al-Dulaimi K, Banks J, Nugyen K, Al-Sabaawi A, Tomeo-Reyes I, Chandran V. Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques. IEEE Rev Biomed Eng 2020; 14:290-306. [PMID: 32746365 DOI: 10.1109/rbme.2020.3004639] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.
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Poostchi M, Ersoy I, McMenamin K, Gordon E, Palaniappan N, Pierce S, Maude RJ, Bansal A, Srinivasan P, Miller L, Palaniappan K, Thoma G, Jaeger S. Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy. J Med Imaging (Bellingham) 2018; 5:044506. [PMID: 30840746 PMCID: PMC6290955 DOI: 10.1117/1.jmi.5.4.044506] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 10/23/2018] [Indexed: 11/28/2022] Open
Abstract
Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.
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Affiliation(s)
- Mahdieh Poostchi
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States
| | - Ilker Ersoy
- University of Missouri-Columbia, Informatics Institute, Missouri, United States
| | - Katie McMenamin
- University of Colorado Boulder, Aerospace Engineering Sciences Department, Boulder, Colorado, United States
| | - Emile Gordon
- National Institute of Allergy and Infectious Diseases, Division of Intramural Research, Rockville, Maryland, United States
| | - Nila Palaniappan
- University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, United States
| | - Susan Pierce
- National Institute of Allergy and Infectious Diseases, Division of Intramural Research, Rockville, Maryland, United States
| | - Richard J. Maude
- University of Oxford, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, Oxford, United Kingdom
- Mahidol University, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Bangkok, Thailand
- Harvard University, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States
| | - Abhisheka Bansal
- Jawaharlal Nehru University, School of Life Sciences, New Delhi, India
- National Institute of Allergy and Infectious Diseases, Laboratory of Malaria and Vector Research, Rockville, Maryland, United States
| | - Prakash Srinivasan
- National Institute of Allergy and Infectious Diseases, Laboratory of Malaria and Vector Research, Rockville, Maryland, United States
- Johns Hopkins Bloomberg School of Public Health, Molecular Microbiology and Immunology, Baltimore, Maryland, United States
| | - Louis Miller
- National Institute of Allergy and Infectious Diseases, Laboratory of Malaria and Vector Research, Rockville, Maryland, United States
| | - Kannappan Palaniappan
- University of Missouri-Columbia, Department of Electrical Engineering and Computer Science, Columbia, Missouri, United States
| | - George Thoma
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States
| | - Stefan Jaeger
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States
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Aliyu HA, Sudirman R, Abdul Razak MA, Abd Wahab MA. Red Blood Cell Classification: Deep Learning Architecture Versus Support Vector Machine. 2018 2ND INTERNATIONAL CONFERENCE ON BIOSIGNAL ANALYSIS, PROCESSING AND SYSTEMS (ICBAPS) 2018. [DOI: 10.1109/icbaps.2018.8527398] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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9
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Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Transl Res 2018; 194:36-55. [PMID: 29360430 PMCID: PMC5840030 DOI: 10.1016/j.trsl.2017.12.004] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 12/07/2017] [Accepted: 12/19/2017] [Indexed: 12/11/2022]
Abstract
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
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Affiliation(s)
- Mahdieh Poostchi
- U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Kamolrat Silamut
- Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Richard J Maude
- Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Stefan Jaeger
- U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland.
| | - George Thoma
- U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland
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Strbkova L, Zicha D, Vesely P, Chmelik R. Automated classification of cell morphology by coherence-controlled holographic microscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:1-9. [PMID: 28836416 DOI: 10.1117/1.jbo.22.8.086008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 07/28/2017] [Indexed: 06/07/2023]
Abstract
In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.
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Affiliation(s)
- Lenka Strbkova
- Brno University of Technology, Central European Institute of Technology, Brno, Czech Republic
| | - Daniel Zicha
- Brno University of Technology, Central European Institute of Technology, Brno, Czech Republic
| | - Pavel Vesely
- Brno University of Technology, Central European Institute of Technology, Brno, Czech Republic
| | - Radim Chmelik
- Brno University of Technology, Institute of Physical Engineering, Faculty of Mechanical Engineering,, Czech Republic
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Das DK, Chakraborty C, Bhattacharya PS. Automated Screening Methodology for Asthma Diagnosis that Ensembles Clinical and Spirometric Information. J Med Biol Eng 2016. [DOI: 10.1007/s40846-016-0137-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Koley S, Sadhu AK, Mitra P, Chakraborty B, Chakraborty C. Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.01.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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13
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Elsalamony HA. Healthy and unhealthy red blood cell detection in human blood smears using neural networks. Micron 2016; 83:32-41. [PMID: 26867209 DOI: 10.1016/j.micron.2016.01.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 01/26/2016] [Accepted: 01/27/2016] [Indexed: 10/22/2022]
Abstract
One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classification of RBCs based on a microscopic examination in blood smears. This paper presents a proposed algorithm for detecting and counting three types of anaemia-infected red blood cells in a microscopic coloured image using circular Hough transform and morphological tools. Anaemia cells include sickle, elliptocytosis, microsite cells and cells with unknown shapes. Additionally, the resulting data from the detection process have been analysed by a prevalent data analysis technique: the neural network. The experimental results for this model have demonstrated high accuracy for analysing healthy/unhealthy cells. This algorithm has achieved a maximum detection of approximately 97.8% of all cells in 21 microscopic images. Effectiveness rates of 100%, 98%, 100%, and 99.3% have been achieved using neural networks for sickle cells, elliptocytosis cells, microsite cells and cells with unknown shapes, respectively.
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Affiliation(s)
- Hany A Elsalamony
- Mathematics Department, Faculty of Science, Helwan University, Cairo, Egypt; Computer Science & Information Department, Arts & Science College, Sattam University, Saudi Arabia.
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14
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Erythrocyte shape classification using integral-geometry-based methods. Med Biol Eng Comput 2015; 53:623-33. [DOI: 10.1007/s11517-015-1267-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 03/02/2015] [Indexed: 10/23/2022]
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15
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Characterization of red blood cells with multiwavelength transmission spectroscopy. BIOMED RESEARCH INTERNATIONAL 2015; 2015:382641. [PMID: 25654099 PMCID: PMC4309305 DOI: 10.1155/2015/382641] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 11/15/2014] [Indexed: 11/30/2022]
Abstract
Multiwavelength transmission (MWT) spectroscopy was applied to the investigation of the morphological parameters and composition of red blood cells (RBCs). The MWT spectra were quantitatively analyzed with a Mie theory based interpretation model modified to incorporate the effects of the nonsphericity and orientation of RBCs. The MWT spectra of the healthy and anemic samples were investigated for the RBC indices in open and blinded studies. When MWT performance was evaluated against a standard reference system, very good agreement between two methods, with R2 > 0.85 for all indices studied, was demonstrated. The RBC morphological parameters were used to characterize three types of anemia and to draw an association between RBC morphology and anemia severity. The MWT spectra of RBCs infected with malaria parasite Plasmodium falciparum at different life cycle stages were analyzed for RBC morphological parameters. The changes in the RBC volume, surface area, aspect ratio, and hemoglobin composition were used to trace the morphological and compositional alterations in the infected RBCs occurring with parasites' development and to provide insights into parasite-host interactions. The MWT method was shown to be reliable for determination of the RBC morphological parameters and to be valuable for identification of the RBC pathologic changes and disease states.
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DAS DK, MAITI AK, CHAKRABORTY C. Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J Microsc 2014; 257:238-52. [PMID: 25523795 DOI: 10.1111/jmi.12206] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 11/16/2014] [Indexed: 11/28/2022]
Affiliation(s)
- D. K. DAS
- School of Medical Science and Technology; Indian Institute of Technology; Kharagpur India
| | - A. K. MAITI
- Midnapur Medical College and Hospital; Midnapur West Bengal India
| | - C. CHAKRABORTY
- School of Medical Science and Technology; Indian Institute of Technology; Kharagpur India
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17
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Mukherjee R. Morphometric evaluation of preeclamptic placenta using light microscopic images. BIOMED RESEARCH INTERNATIONAL 2014; 2014:293690. [PMID: 25050336 PMCID: PMC4094848 DOI: 10.1155/2014/293690] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2014] [Revised: 05/25/2014] [Accepted: 05/26/2014] [Indexed: 01/15/2023]
Abstract
Deficient trophoblast invasion and anomalies in placental development generally lead to preeclampsia (PE) but the inter-relationship between placental function and morphology in PE still remains unknown. The aim of this study was to evaluate the morphometric features of placental villi and capillaries in preeclamptic and normal placentae. The study included light microscopic images of placental tissue sections of 40 preeclamptic and 35 normotensive pregnant women. Preprocessing and segmentation of these images were performed to characterize the villi and capillaries. Fisher's linear discriminant analysis (FLDA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were applied to identify the most significant placental (morphometric) features from microscopic images. A total of 10 morphometric features were extracted, of which the villous parameters were significantly altered in PE. FLDA identified 5 highly significant morphometric features (>90% overall discrimination accuracy). Two large subclusters were clearly visible in HCA based dendrogram. PCA returned three most significant principal components cumulatively explaining 98.4% of the total variance based on these 5 significant features. Hence, quantitative microscopic evaluation revealed that placental morphometry plays an important role in characterizing PE, where the villous is the major component that is affected.
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Affiliation(s)
- Rashmi Mukherjee
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
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