Fatty Liver Characterization and Classification by Ultrasound

Ribeiro, Ricardo; Sanches, J. Miguel

doi:10.1007/978-3-642-02172-5_46

Cited by 38 publications

(24 citation statements)

References 13 publications

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“…accurate and 100% sensitive. As compared to a recently published work by Ribero and Sanches [16], where the reported accuracy is 95 % and sensitivity is 100% using Bayesian classifier, however the proposed method is much simpler and faster. The only drawback of the proposed method is that their characterization criterion is machine dependent.…”

Section: A New Metricmentioning

confidence: 57%

A New Quantitative Metric for Liver Classification from Ultrasound Images

Singh¹,

Singh²,

Gupta³

2012

IJCEE

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Abstract-This paper presents a new quantitative metric which can be used as indirect method for characterization of liver from ultrasound images. This new metric is inspired from the visual criterion considered by the radiologists through texture and echogenicity of the Liver in ultrasound image. The proposed metric is a single parameter extracted from 6 texture features on the basis of Homogeneity, Roughness, Contrast, Granularity and Orientation of the liver surface for classification into Fatty or Normal liver. The main contribution of the present work is that the best texture features are selected on the basis of Fisher's linear discriminative analysis and then formulated into a single metric by considering their weightage similar to the visual criterion used for liver classification.

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Section: A New Metricmentioning

confidence: 57%

A New Quantitative Metric for Liver Classification from Ultrasound Images

Singh¹,

Singh²,

Gupta³

2012

IJCEE

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“…Ribeiro and Sanched [28] proposed an automatic classification approach for diagnosing the liver steatosis (fatty liver) from US images. Since physician's diagnosis depends on some characteristics using traditional methods (visual inspection of US images), the features are selected to obtain the similar characteristics utilized by the doctors.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Segmentation for Liver Diseases: A Survey

Hassan¹,

Elmogy²,

Sallam³

2015

IJCA

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“…In this type of images, involving coherent radiation, the speckle (pseudo)-noise corrupting the image is assumed to be multiplicative in the sense that its variance depends on the underlying noiseless image [6]. Therefore, the observation model assumed in this paper is the following…”

Section: A Pre-processing Algorithmmentioning

confidence: 99%

“…Laboratorial Information [9] Total bilirubin (F 19 ) , prothrombin time (F 20 ) , albumin (F 21 ) , creatinine (F 22 ) , aspartate transaminase (F 23 ), alanine transaminase (F 24 ), gamma glutamyl transpeptidase (F 25 ) , glycemia (F 26 ), sodium (F 27 ), urea (F 28 ) and lactate dehydrogenase(F 29 ). Clinical Information [9] Cause of disease (F 30 ), which include none (0), alcohol (1), hepatitis B (2), hepatitis C (3), alcoholic hepatitis B (4) and C (5) and others (6), and the following binary indicators: Tumor (F 31 ), Ascites (F 32 ), presence of free fluid within the peritoneal cavity; encephalopathy (F 33 ), Gastro-Intestinal bleeding (F 34 ) infection (F 35 ), alcoholic habits (F 36 ) and CHILD score (F 37 ).…”

Section: B Feature Extraction and Selectionmentioning

confidence: 99%

The usefulness of ultrasound in the classification of chronic liver disease

Ribeiro¹,

Marinho

Velosa

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

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Fatty Liver Characterization and Classification by Ultrasound

Cited by 38 publications

References 13 publications

A New Quantitative Metric for Liver Classification from Ultrasound Images

A New Quantitative Metric for Liver Classification from Ultrasound Images

Medical Image Segmentation for Liver Diseases: A Survey

The usefulness of ultrasound in the classification of chronic liver disease

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