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

Krishnan, Kannan; Sudhakar, R.

doi:10.1049/iet-ipr.2016.1072

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…Intensity, local phase and phase congruency features define each voxel in registration for tumor tracking. 125 A block matching approach was applied for defining the spatial correspondence in each extracted feature. The final transformation is obtained by combining the local contribution of all the features.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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“…Two such features namely, multi-level fractal features, which are fractal features extracted from multiple threshold versions of the image and multi-domain features-a hybrid combination of texture features extracted from wavelet filtered image components were investigated in our earlier work and were found to give marginally better classification results over conventional intensity-based features. 50,51 These two features are discussed briefly in the following sub-sections, which will be used in optimization-based classification after min-max normalization. 52…”

Section: Feature Descriptionmentioning

confidence: 99%

“…54 The separated ROIs thus obtained are used for the extraction of texture features. Among the different texture features available, GLRLM features prove to be effective in giving better classification rates in ultrasound images 51,[55][56][57][58] . The combination of GLRLM features with biorthogonal wavelet filtered ROIs has been demonstrated to significantly improve the classification results.…”

Section: Multi-domain Featuresmentioning

confidence: 99%

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Particle swarm optimization‐based liver disorder ultrasound image classification using multi‐level and multi‐domain features

Krishnamurthy

Sudhakar²,

Kadhar³

2020

Int J Imaging Syst Tech

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Liver ultrasound is a cost-effective, non-invasive, and sufficient technique to diagnose most of the liver disorders. The recent advancements in research in image processing have led to the development of image-based liver disorder classification systems. In spite of being popular in the diagnostic imaging of liver, ultrasound images, owing to their poor quality, render the conventional and state of the art segmentation and feature extraction techniques incapable, to accurately classify a large mixed group of liver disorders; due to the similarities and differences in appearances among the different and same disorders, respectively. Classification of liver disorders using ultrasound images poses various challenges at each phase, from segmentation to classification. There is a need for better segmentation, powerful features, and optimal classification parameter combinations to obtain decent classification accuracy, when a large sub-set of liver disorders is considered. In this work, the region of interest is extracted using iso-contour technique. Feature extraction is performed using multi-level fractal features and multi-domain wavelet-texture features for better discrimination capability. Then, an optimization problem is formulated, for minimizing the five fold cross validation error to classify 10 types of disorders, both focal and diffused, by selecting the best features, suitable classifier, and its parameters using the particle swarm optimization technique for obtaining better classification. An overall accuracy of 91% is obtained using the proposed features in addition to 50% reduction in multi-level fractal feature set which justifies the efficacy of the proposed technique.

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“…In 2015, deaths caused by liver diseases due to alcohol were reported diagnosis. To classify ten various types of focal and diffused liver disorders, Raghesh Krishnan and Radhakrishnan [39] implemented a hybrid approach to classifying focal and diffused liver disorders. Initially, the disease region was separated from the ultrasound image by applying the active contour segmentation technique.…”

Section: Introductionmentioning

confidence: 99%

Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

Rajathi¹,

Jiji²

2019

Symmetry

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Chronic liver disease (CLD), which indicates the inflammatory condition of the liver, leads to cirrhosis or even partial or total liver dysfunction when left untreated. A non-invasive approach for evaluating CLD with computed tomography (CT) images is proposed using an ensemble of classifiers. To accurately classify CLD, the hybrid whale optimization algorithm with simulated annealing (WOA-SA) is used in selecting an optimal set of features. The proposed method employs seven sets of features with a total of 73–3D (three-dimensional) texture features. A hybrid ensemble classifier with support vector machine (SVM), k—Nearest Neighbor (k-NN), and random forest (RF) classifiers are used to classify liver diseases. Experimental analysis is performed on clinical CT images datasets, which include normal liver, fatty liver, metastasis, cirrhosis, and cancerous samples. The optimal features selected using the WOA-SA improve the accuracy of CLD classification for the five classes of diseases mentioned above. The accuracy of the liver classification using ensemble classifier yields approximately 98% with a 95% confidence interval (CI) of (0.7789, 1.0000) and an error rate of 1.9%. The performance of the proposed method is compared with two existing algorithms and the sensitivity and specificity yield an overall average of 96% and 93%, with 95% confidence interval of (0.7513, 1.0000) and (0.7126, 1.0000), respectively. Classification of CLD based on ensemble classifier illustrates the effectiveness of the proposed method and the comparison analysis demonstrates the superiority of the methodology.

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

Cited by 27 publications

References 20 publications

Carotid artery ultrasound image analysis: A review of the literature

Carotid artery ultrasound image analysis: A review of the literature

Particle swarm optimization‐based liver disorder ultrasound image classification using multi‐level and multi‐domain features

Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

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