Classification of Liver Diseases Based on Ultrasound Image Texture Features

Xu, Sendren Sheng Dong; Chang, Chun Chao; Su, Chien Tien; Phu, Pham Quoc

doi:10.3390/app9020342

Cited by 37 publications

(17 citation statements)

References 51 publications

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“…Details of the feature extraction are given in Ref. 7. The F-score of the GLCM feature extraction method was as high as 0.225.…”

Section: Resultsmentioning

confidence: 99%

“…From each of the ROIs, we extracted 96 features (52 GLCMs and 44 GLRLMs). The GLCM (7)(8)(9) is represented by a matrix depicting how different combinations of gray levels exist in an image.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The GLCM feature extraction results, also called the Haralick features, are extracted from each image and shown in Table 1. The other method employed in this research is the use of the GLRLM (7,9) to compute four matrices for horizontal, vertical, and diagonal directions, i.e., 0, 45, 90, and 135°, to produce a run-length matrix. The results of the calculation are called texture descriptors, and each descriptor is unique for each texture.…”

Section: Feature Extractionmentioning

confidence: 99%

“…(6) Here, we propose NN-based classification for the CAD of images obtained from ultrasound imaging, which has several advantages over liver biopsy such as no radiation risk, low cost, easy operation, and non-invasiveness. We applied the gray-level co-occurrence matrix (GLCM) (7)(8)(9) and the gray-level run-length matrix (GLRLM) (7,10) as textural features with three feature selection models: sequential forward selection (SFS), (7,9,11) sequential backward selection (SBS), (7,9,12) and F-score. (7,13)…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images

Xu¹,

Chang²,

Su³

et al. 2020

Sensors and Materials

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In diagnostic ultrasound, an ultrasound transducer converts an electrical signal into an ultrasound pulse, which enters the tissue from the body surface. At the surface, an echo appears. The probe senses and receives the echo, and all the echoes are converted back to signals and graphics, which can be analyzed by medical staff. We studied the neural network (NN)-based classification of hepatocellular carcinoma (HCC) and liver abscess using texture features of ultrasound images. From 79 cases of liver diseases (44 liver cancer and 35 liver abscess cases), we extracted 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM), giving a total of 96 features. We used three feature selection models to distinguish these two liver diseases: sequential forward selection (SFS), sequential backward selection (SBS), and F-score. We proved that our developed system can be used to classify liver cancer and liver abscess using an NN with an accuracy of 88.375%, which can provide diagnostic assistance for inexperienced clinicians.

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“…Details of the feature extraction are given in Ref. 7. The F-score of the GLCM feature extraction method was as high as 0.225.…”

Section: Resultsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images

Xu¹,

Chang²,

Su³

et al. 2020

Sensors and Materials

Self Cite

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show abstract

“…A hybrid combination of GLCM and GLRLM, selected using sequential forward selection, F-score, sequential backward selection and classified using SVM has reported an accuracy of 88.875% using F-score, in the classification of liver cancer and liver abscess. 31 Transform-based approaches are commonly used in the classification of normal and cirrhotic ROIs. Quincunx transform, a non-separable wavelet transform is used in the classification of visually similar diffuse disorders and is proven suitable for noisy data characterization.…”

Section: Introductionmentioning

confidence: 99%

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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Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

Varchagall

Yogegowda

2022

Concurrency and Computation

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Summary Human liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD‐HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD‐HS method using well‐known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state‐of‐the‐art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1‐score, G‐mean, and area under curve (AUC). The detection accuracy of the proposed HSSI‐DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).

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Classification of Liver Diseases Based on Ultrasound Image Texture Features

Cited by 37 publications

References 51 publications

Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images

Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images

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

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

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