Classification of liver diseases using ultrasound images based on feature combination

Alivar, Alaleh; Daniali, Habibollah; Helfroush, Mohammad Sadegh

doi:10.1109/iccke.2014.6993434

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…Existing computer-aided cirrhosis diagnosis systems mainly focus on quantitatively analyzing the texture of parenchyma in liver ultrasound images by extracting texture features such as fractal features [ 7 ], statistical texture features [ 8 , 9 , 10 ], spectral features [ 11 , 12 , 13 , 14 ] or combined features [ 2 , 15 , 16 , 17 , 18 ]. Then different classifiers such as random forest, support vector machine, neural networks, etc., are trained to classify the samples into cirrhosis or normal ones.…”

Section: Related Workmentioning

confidence: 99%

“…These fractal dimension based method achieved satisfying performance in distinguishing normal and abnormal liver, but better methods are needed to further determine the cirrhosis stages. Several proposals combine several feature extraction methods together [ 2 , 15 , 16 , 17 , 18 ], which make use of the advantages of different features. However, the performance relies on a good feature selection method such as genetic algorithm [ 14 , 19 ], singular value decomposition [ 9 ], particle swarm optimization [ 17 ], etc.…”

Section: Related Workmentioning

confidence: 99%

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Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

Liu

Song

Wang

et al. 2017

Sensors

105

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This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

Liu

Song

Wang

et al. 2017

Sensors

105

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show abstract

“…It also used and integrated nearly seven textures during the fusion of the segmented images in order to ensure doi:10.32377/cvrjst1512 the significant classification rate of 96%. Then Alivar et al [12] devoted a scheme that used ROI size of 64X64 pixels during segmentation. Then ROI images are carefully used for determining different quantifiable wavelet packet, Gray Level Co-occurrence Matrix and Gabor Transform for phenomenal feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

Rajesh¹

2018

CVRJST

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In the treatment of abdominal diseases like fatty liver disorder, ultrasonic images-based investigation is considered as the primary step of diagnosis. But, the noisy feature of ultrasonic images combined with the least contrasting features introduces maximum complexity during the process of automated classification. This paper contributes an Improved Active Contour Segmentation scheme for effective segmentation. Then Gray Level Co-occurrence Matrix (GLCM) and fractal features are extracted from the segmented ultrasonic images and the classification is achieved using an enhanced forest classification incorporated in the artificial neural networks (ANN) for accurate detection. The results of the proposed automated approach are investigated using classification accuracy, mean classification accuracy, true positive rate, false positive rate and true negative rate. The results of the proposed scheme also infers a classification accuracy rate of 98.73% and a mean classification accuracy rate of 97.65% compared to the baseline automated liver disorder classification techniques.

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“…A fusion of seven texture models extracted from the segmented images gives a classification accuracy of 96%. In [2], an ROI of size 64 × 64 pixels is manually selected from which wavelet packet, Gabor transform and grey‐level co‐occurrence matrix (GLCM) features are extracted. A dataset of 39 samples of fatty liver, 30 normal liver samples and 7 cirrhotic samples are considered.…”

Section: Related Workmentioning

confidence: 99%

Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features

Krishnan

Sudhakar²

2017

IET Image Processing

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Classification of liver diseases using ultrasound images based on feature combination

Cited by 14 publications

References 8 publications

Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features

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