Journal of Pathology Informatics Journal of Pathology Informatics
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REVIEW ARTICLE
Year : 2014  |  Volume : 5  |  Issue : 1  |  Page : 9

Peripheral blood smear image analysis: A comprehensive review


1 Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
2 Department of Biomedical Engineering, Faculty of Engineering, Helwan University, Cairo, Egypt
3 Department of Pathology and Laboratory Medicine, Calgary Laboratory Services, University of Calgary, Calgary, Alberta, Canada

Correspondence Address:
Christopher Naugler
Department of Pathology and Laboratory Medicine, Calgary Laboratory Services, University of Calgary, Calgary, Alberta
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.129442

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Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM) and artificial neural networks (ANNs) are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process: pattern classification. The goal of pattern classification is to assign a class to the selected features from a group of known classes. There are two types of classifier learning algorithms: supervised and unsupervised. Supervised learning algorithms predict the class of the object under test using training data of known classes. The training data have a predefined label for every class and the learning algorithm can utilize this data to predict the class of a test object. Unsupervised learning algorithms use unlabeled training data and divide them into groups using similarity measurements. Unsupervised learning algorithms predict the group to which a new test object belong to, based on the training data without giving an explicit class to that object. ANN, SVM, decision tree and K-nearest neighbor are possible approaches to classification algorithms. Increased discrimination may be obtained by combining several classifiers together.


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