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Texture analysis for liver segmentation

and classification: a survey

Saima Rathore, Muhammad Aksam Iftikhar,

Mutawarra Hussain, Abdul JalilPakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad


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Abstract

Texture is a combination of repeated patterns with regular/irregular

frequency. It can only be visualized but hard to describe in words. Liver

structure exhibit similar behavior; it has maximum disparity in intensity

texture inside and along boundary which serves as a major problem in its

segmentation and classification. The problem of representing liver textureis solved by encoding it in terms of certain parameters (called features) for

texture analysis. Numerous texture analysis techniques have been devised

for liver classification over the years some of which work equally work

well for most of the imaging modalities. In this paper, we attempt to

summarize the efficacy of textural analysis techniques devised for CT,

Ultrasound and some other imaging modalities like MRI, in terms of well-known performance metrics.


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Introduction

Liver is the largest organ of body and performsvarious critical bodily functions

Normal liver usually differs from the diseasedone in terms of intensity texture. Thisvariation helps in determining thecorresponding disease.

A Computer-Aided-Diagnosis (CAD) system is amerger of medical imaging and tissuecharacterization techniques


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Liver CAD system

Figure 1 shows top level layout of a Computer

Aided Diagnosis system employing liver

texture analysis for disease diagnosis

Figure 1


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Textural analysis techniques

Various textural analysis techniques have beenused in literature which give rise to different setof features for liver classification. A few populartechniques are named as follows. Gray Level Difference Statistics (GLDS)

Spatial Gray level Dependence Matrices (SGLDM)

Gray level Run length Statistics (RUNL)

Laws Texture Energy Measure (TEM)

Wavelet Features

Fourier Power Spectrum (FPS)

First-Order Parameters (FOP)


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Texture Features

Aforementioned techniques give rise to various

features for texture classification. E.g.

Entropy (ENT)

Run Length Distribution (RLD)

Contrast (CO)

Variance (VAR)

Energy (E)

Uniformity (U)

Short Run Emphasis (SRE)

Gray Level Distribution (GLD)

Angular Second Moment (ASM)

Correlation (CORR)

Standard Deviation (SD)

Homogeneity (H)

Mean (M)

LAWs textural energy features


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US Texture Analysis Techniques

Ultrasound is the least expansive and most available

medical imaging modality

It has been used most frequently by researchers for liver

texture analysis . Table 1 (on next slide) is a summary of the work with US

modality for classification of different liver diseases


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US Texture Analysis Techniques

Paper Features / Technique Liver Classes

[2] GLDS, FDTA Normal, Cirrhosis, Heptoma

[5] GLDS, FDTA, RUNL, SGLDM Normal, Fatty, Cirrhosis, Heptoma

[6] GLDS, SGLDM, FDTA, RUNL, FOP Normal, Fatty, Cirrhosis

[7] FDTA, SGLDM Normal, Fatty, Cirrhosis

[24] GLDS, FDTA Normal, Cirrhosis, Heptoma

[29] Wavelet Normal, Cirrhosis, Steatosis

[30] SGLDM, FDTA, RUNL, GLDS Normal, Fatty, Cirrhosis, Heptoma

[31] GLDS, SGLDM, RUNL, FOP Normal, Fatty, Cirrhosis

[34] FDTA, SGLDM Normal, Fatty, Cirrhosis

[37] Gabor Wavelet Normal, Diseased

[42] Wavelet Normal, Diseased

[43] GLDS, SGLDM, Histogram Normal, Fatty

Table 1


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CT Texture Analysis Techniques

Computed Tomography (CT) is reliable enough but expansivemedical imaging modality

Table 2 shows different research works employing different textureanalysis techniques for classification of different liver disease

Paper Features / Technique Liver Classes

[6] FOP,SGLDM, GLDM,TEM, and FDTA Normal, Fatty, Cirrhosis

[9] Wavelets Normal, Diseased

[10] Wavelets Normal, Diseased

[13] Zernike moments, Legendremoments

Normal, HepatocellularCarcinoma

[14] SGLDM Normal, Cirrhosis, Heptoma,

Hemangioma

[19] FOP Normal, Heptoma,

Hepatocellular Carcinoma

Table 2


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Other Modalities

Several other modalities have been tried byresearchers, but with less frequency, to classify liverinto different liver classes.

Table 3 presents a summary of such effortsPaper Modality Features / Technique Liver Classes

[9] MRI GLDS, Shape features Noraml, Cirrhosis

[47] Biopsy FDTA Normal, Hepatocellular

Carcinoma[49] Mammography SGLDM Normal, Diseased

Table 3


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Discussion

Authors have employed a variety of textureanalysis techniques for liver classification

Different authors have used differentperformance metrics to check classification

accuracy. Most have used classification accuracyas a measure of performance while a few haveexploited area under ROC curve.

A consistent measure of accuracy would have

been more helpful for comparative analysis A summary of classification results using different

textural measures can be of substantial value forsetting future directions.


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A very few authors have used

sensitivity/specificity as performance measure,

which is summarized in table 4 below

Results Summary / Comparative Analysis

Paper Modality Features / Technique Sensitivity Specificity

[9] CT Wavelets 96 94

[10] CT Wavelets 98 85

[36] US SGLDM, FDTA 94.9 81.3

[37] US Gabor Wavelet 85.5 78

Table 4


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Results Summary / Comparative Analysis

Following graphical summary (Figure 2) of results provides greatcomfort to gain a deeper insight into the performance (accuracy)

of different research efforts and compare them critically.

Highlighted results show better performance of two techniques(One Ultrasound and one Computed Tomography)

Legend: Normal, Fatty, Cirrhosis, Heptoma, TotalFigure 2


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Best Performance Technique for CT

Bharathi et al. [13] utilized the better featurerepresentation capability and least informationredundancy of Zernike moments and Legendremoments for classification of normal and HCC liver

using CT images. Total 200 ROIs (140 Normal, 60 HCC) were

experimented out of which 75 were used for trainingand remaining for testing

The classification result with Zernike and Legendrefeature vector for normal liver was 98.60% and 97.57%respectively

Classification accuracy for HCC was 90.00% and 81.5%.


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Best Performance Technique for US

Mojsilovik et al. [29] used 6-level quincunx

wavelet decomposition for identifying diffused

liver diseases in their work.

They estimated channel variances using wavelets

at the output of each filter of the filter bank

which were then used for liver classification.

This scheme was effective as well as simple as itclassified normal and cirrhosis liver images with

an accuracy of 94% and 90% respectively.


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Conclusion

It has been observed that techniques based on CT textureanalysis, though evaluated for a few liver diseases, havemuch better discriminating power than others.

Contrary, techniques based on ultrasound images have

been used for diagnosing a large number of diseases (thismight pertain to low cost of ultrasound) but are lessaccurate.

Moreover, texture measure methods perform better whenused in combination as compared to their standalone

application. As already indicated that statistical moments based

technique gives better performance in case of CT[13] whilein case of ultrasound wavelet feature extraction [29]outclasses others


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Future Work

Current survey of liver textural analysis can be

extended in two directions in future

testing all texture measure methods using same

data set and similar performance measures may

provide a more accurate analysis

Adding more texture measure methods, even

trying a new one, can potentially provide bettercomparative study and a useful addition to current

research database


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