NDLI: A Liver Segmentation Algorithm Based on Wavelets and Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Suhuai Luo Jin, J.S. Chalup, S.K. Guoyu Qian
Copyright Year	2009
Abstract	This paper introduces an automatic liver parenchyma segmentation algorithm that can delineate liver in abdominal CT images. The proposed approach consists of three main steps. Firstly, a texture analysis is applied onto input abdominal CT images to extract pixel level features. Here, two main categories of features, namely Wavelet coefficients and Haralick texture descriptors are investigated. Secondly, support vector machines (SVM) are implemented to classify the data into pixel-wised liver or non-liver. Finally, specially combined morphological operations are designed as a post processor to remove noise and to delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present better classification than Haralick texture descriptors when SVMs are used; the other is that the combination of morphological operations with a pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and surgical planning systems. Examples of applying the algorithm on real CT data are presented with performance validation based on the automatically segmented results and that of manually segmented ones.
Starting Page	122
Ending Page	125
File Size	394629
Page Count	4
File Format	PDF
ISBN	9780769536453
DOI	10.1109/CINC.2009.225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms morphology Liver machine learning Abdomen Morphological operations Support vector machines Image segmentation Image analysis Computed tomography Support vector machine classification Machine learning wavelet segmentation texture analysis
Content Type	Text
Resource Type	Article

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