TITLE

Peripheral blood smear image analysis: A comprehensive review

AUTHOR(S)
Mohammed, Emad A.; Mohamed, Mostafa M. A.; Far, Behrouz H.; Naugler, Christopher
PUB. DATE
January 2014
SOURCE
Journal of Pathology Informatics;2014, Vol. 5 Issue 1, p60
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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.
ACCESSION #
97722823

 

Related Articles

  • Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping. Shih, Hsiao-chien; Stow, Douglas A.; Tsai, Yu Hsin // International Journal of Remote Sensing;Feb2019, Vol. 40 Issue 4, p1248 

    Remote sensing scientists are increasingly adopting machine learning classifiers for land cover and land use (LCLU) mapping, but model selection, a critical step of the machine learning classification, has usually been ignored in the past research. In this paper, step-by-step guidance (for...

  • Evaluation of Different Machine Learning Methods for Caesarean Data Classification. Alsharif, O. S. S.; Elbayoudi, K. M.; Aldrawi, A. A. S.; Akyol, K. // International Journal of Information Engineering & Electronic Bu;Sep2019, Vol. 11 Issue 5, p19 

    Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved...

  • Object-Based Image Classification of Summer Crops with Machine Learning Methods. Peña, José M.; Gutiérrez, Pedro A.; Hervás-Martínez, César; Six, Johan; Plant, Richard E.; López-Granados, Francisca // Remote Sensing;Jun2014, Vol. 6 Issue 6, p5019 

    The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by...

  • Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery. Mojaddadi Rizeei, Hossein; Pradhan, Biswajeet; Saharkhiz, Maryam Adel // International Journal of Remote Sensing;Feb2019, Vol. 40 Issue 3, p1092 

    A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally...

  • Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong. Wong, Frankie K. K.; Fung, Tung // International Journal of Remote Sensing;Dec2014, Vol. 35 Issue 23, p7828 

    Mangrove habitat is one of the most highly productive ecosystems. The distribution of mangrove species acts as an inventory to formulate conservation management plans. This study explored the potential of combining hyperspectral (Earth-observing (EO)-1 Hyperion) and multi-temporal synthetic...

  • A machine learning approach to analyze customer satisfaction from airline tweets. Kumar, Sachin; Zymbler, Mikhail // Journal of Big Data;7/17/2019, Vol. 6 Issue 1, pN.PAG 

    Customer's experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer's...

  • Electrical Load Forecasting Using Support Vector Machines: a Case Study. Türkay, Belgin Emre; Demren, Dilara // International Review of Electrical Engineering;Sep/Oct2011 Part B, Vol. 6 Issue 5, p2411 

    In this study, an application with electrical load forecasting is made by a machine learning method that has recently become popular: Support Vector Machines (SVM). Load forecasting with SVM can model the nonlinear relation with the factors that affect the load in addition to the accurate...

  • Classification Methods Based on Pattern Recognition and on Neural Networks for Failure Detection. Dhifallah, Jihane Ben Slimane; Laabidi, Kaouther; Lahmari, Moufida Ksouri // International Review on Computers & Software;May2010, Vol. 5 Issue 3, p257 

    In this article, we propose the use of classification methods for the detection of malfunctioning modes of complex systems. The problem of failure diagnosis is defined, in first place, as a pattern recognition problem, by means of "Support Vector Machines SVM" classifier. Our objective is to...

  • Adaptive protection combined with machine learning for microgrids. Kai Sun; Hengwei Lin; Chengxi Liu; Guerrero, Josep M.; Vasquez, Juan C.; Zheng-Hua Tan // IET Generation, Transmission & Distribution;2019, Vol. 13 Issue 6, p770 

    This paper presents a rule-based adaptive protection scheme using machine-learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so-called...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics