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2018 | OriginalPaper | Buchkapitel

A Review: Image Analysis Techniques to Improve Labeling Accuracy of Medical Image Classification

verfasst von : Mazniha Berahim, Noor Azah Samsudin, Shelena Soosay Nathan

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

Medical images contain the Region of Interest (ROI) from the affected area in human body and provide useful information to support clinical decision-making for diagnostics as well as the treatment planning. Unfortunately, medical image data may contain noise, missing values, inhomogeneous ROI that may give inaccurate diagnostic. Therefore, image analysis techniques are needed to improve the quality of an image. Then, features extraction task will be performed to produce best feature of images which leads to better classification result for accurate diagnostic. Many techniques have been used for image analysis. However, limited review have been done in categorize the list of related techniques for each image analysis task in medical imaging application. Thus, the aims of this paper is to gather and present general overview of image analysis task and their techniques in order to inspire researcher, pathologist or radiologist to adapt it when analyzing different types of medical image. The current study of image analysis task was summarized and discussed in this paper.

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Titel: A Review: Image Analysis Techniques to Improve Labeling Accuracy of Medical Image Classification
verfasst von: Mazniha Berahim
Noor Azah Samsudin
Shelena Soosay Nathan
Verlag: Springer International Publishing
Buch: Recent Advances on Soft Computing and Data Mining
Print ISBN: 978-3-319-72549-9

Electronic ISBN: 978-3-319-72550-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-72550-5_29

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