A New Approach to Ultrasonic Liver Image Classification

Jiann-Shu LEE; Yung-Nien SUN; Xi-Zhang LIN

A New Approach to Ultrasonic Liver Image Classification

Jiann-Shu LEE, Yung-Nien SUN, Xi-Zhang LIN

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In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.

Publication: IEICE TRANSACTIONS on Information Vol.E83-D No.6 pp.1301-1308

Publication Date: 2000/06/25

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Type of Manuscript: PAPER

Category: Medical Engineering

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Jiann-Shu LEE, Yung-Nien SUN, Xi-Zhang LIN, "A New Approach to Ultrasonic Liver Image Classification" in IEICE TRANSACTIONS on Information, vol. E83-D, no. 6, pp. 1301-1308, June 2000, doi: .
Abstract: In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_6_1301/_p

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@ARTICLE{e83-d_6_1301,
author={Jiann-Shu LEE, Yung-Nien SUN, Xi-Zhang LIN, },
journal={IEICE TRANSACTIONS on Information},
title={A New Approach to Ultrasonic Liver Image Classification},
year={2000},
volume={E83-D},
number={6},
pages={1301-1308},
abstract={In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.},
keywords={},
doi={},
ISSN={},
month={June},}

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TY - JOUR
TI - A New Approach to Ultrasonic Liver Image Classification
T2 - IEICE TRANSACTIONS on Information
SP - 1301
EP - 1308
AU - Jiann-Shu LEE
AU - Yung-Nien SUN
AU - Xi-Zhang LIN
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2000
AB - In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.
ER -