Cirrhosis recognition of liver ultrasound images based on SVM and uniform LBP feature

Lei, Yiming; Zhao, Xiangmo; Guo, Weidong

doi:10.1109/iaeac.2015.7428580

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…19,20 Further hybrid combinations of GLCM features and Gray Level Run Length Matrix (GLRLM) features, Law's Texture Energy Measures, Gabor Features, uniform local binary patterns, Hurst's Coefficients, and so forth have been experimented to improve the discrimination capability of the classifier. 9,[21][22][23][24][25][26][27][28][29] Fusion of complementing information extracted from Gray Level Difference Matrix (GLDM) variants prove to be efficient in the enhancement of visualization capability and characterization of cirrhosis, chronic liver, normal liver and HCC progressed to cirrhosis and report an accuracy of 94.5%. 10 Statistical analysis based on intensity difference proves its applicability in cirrhotic liver discrimination.…”

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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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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Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

et al. 2023

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Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.

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Segmentation and classification of knee joint ultrasonic image via deep learning

Long

Zhang

et al. 2020

Applied Soft Computing

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Cirrhosis recognition of liver ultrasound images based on SVM and uniform LBP feature

Cited by 10 publications

References 12 publications

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

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

Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

Segmentation and classification of knee joint ultrasonic image via deep learning

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