Hepatic Steatosis detection using the co-occurrence matrix in tomography and ultrasound images

Rivas, Elymar C.; Moreno, Franklin; Benítez, Alimar; Morocho, Villie; Vanegas, Pablo; Medina, Rubén

doi:10.1109/stsiva.2015.7330417

Cited by 11 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, ROIs are chosen manually by an expert or automatically using methods like a self-organizing map [ 53 ]. In this research, some studies used the complete image as the ROI with a size of 434 × 636 pixels [ 60 , 61 , 63 , 64 ], 800 × 600 pixels [ 71 ], 960 × 720 pixels [ 51 ], or 1024 × 1024 pixels [ 39 ]. Other studies selected larger ROIs with 500 × 500 pixels [ 36 , 37 , 53 ].…”

Section: Resultsmentioning

confidence: 99%

“…Features such as maximum probability [ 38 , 52 , 53 , 59 , 63 , 78 , 80 ], uniformity [ 38 , 52 , 53 , 59 , 60 , 63 , 70 , 80 ], entropy [ 37 , 38 , 49 , 52 , 59 , 63 , 66 , 70 , 71 , 73 , 74 , 76 , 80 , 82 ], contrast [ 38 , 49 , 52 , 59 , 63 , 65 , 66 , 70 , 71 , 73 , 74 , 76 , 78 , 80 ], run-length uniformity [ 38 , 52 , 70 , 80 ], attenuation [ 43 , 73 , ...…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

AlShagathrh

Househ

2022

Bioengineering

View full text Add to dashboard Cite

Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

AlShagathrh

Househ

2022

Bioengineering

View full text Add to dashboard Cite

show abstract

“…The classification of liver tumors images has become important in recent years. Many studies have been done with conventional image processing methods [26,27,28,29]. Many of the studies, like ours, used custom datasets.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Kutlu

Avcı

2019

Sensors

View full text Add to dashboard Cite

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Recently, several deep learning-based artificial intelligence approaches have been introduced in the literature [ 20 , 21 , 22 , 23 , 24 , 25 ] to overcome the issues and challenges associated with US image quality and operator dependency. Andrea et al [ 20 ] proposed a computer-aided diagnosis (CAD) system based on feature extraction to assist in the classification task of liver pathologies. The incorporated feature extraction is based on first-order statistics, co-occurrence matrix, run-length matrix, and fractal dimensions, where three different classifiers are used for the evaluation of certain features, including artificial neural network, support vector machine (SVM), and k-nearest neighbor.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images

Rhyou

Yoo

2021

Sensors

View full text Add to dashboard Cite

Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.

show abstract

Hepatic Steatosis detection using the co-occurrence matrix in tomography and ultrasound images

Cited by 11 publications

References 10 publications

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images

Contact Info

Product

Resources

About