Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques

Caixinha, Miguel; Santos, Mário; Santos, Jaime

doi:10.1016/j.ultrasmedbio.2015.11.021

Cited by 12 publications

(9 citation statements)

References 53 publications

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“…Linear discrimination analysis (LDA) [ 40 ] and logistic regression (LOGREG) [ 41 ] are two of the most widely used linear classifiers in the ultrasound CAD system. LDA is proposed by Fisher and is extensively used in medical image analysis [ 32 , 42 ]. It aims to find the best linear combination of the features to divide the data into several categories.…”

Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

162

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The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

162

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“…The ultrasound backscattering signal [12][13][14][15][16] is used for cataract assessment based on the animal model. By using probability density features and multiclass classifiers, the accuracy of cataract hardness assessment achieves 95% when using a small training set.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical method for cataract grading based on retinal images using improved Haar wavelet

Cao

Zhang

et al. 2020

Information Fusion

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Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the improved Haar wavelet. A hierarchical strategy is used to transform the four-class classification problem into three adjacent two-class classification problems. Three sets of two-class classifiers based on a neural network are trained individually and integrated together to establish a complete classification system. The accuracies of the two-class classification (cataract and non-cataract) and four-class classification are 94.83% and 85.98%, respectively.The performance analysis demonstrates that the improved Haar wavelet feature achieves higher accuracy than the original Haar wavelet feature, and the fusion of three sets of two-class classifiers is superior to a simple four-class classifier. The discussion indicates that the retinal image-based method offers significant potential for cataract detection.

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“…16 Ultrasound Nakagami imaging has been used systematically in various medical applications by different research groups. 8,9,[17][18][19][20][21][22][23][24][25][26] Concurrently, some technical issues have been investigated to improve the performance of ultrasound Nakagami imaging, including three-dimensional (3D) Nakagami imaging, 27 artifact reduction, 7,28 small-window Nakagami imaging, 29 and window-modulated compounding (WMC) Nakagami imaging. 6 A literature review indicated that image resolution and smoothness are two foci for research on Nakagami imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Visualization of Ultrasound Backscatter Statistics by Window-modulated Compounding Nakagami Imaging

Zhou

Lin

et al. 2018

Ultrason Imaging

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In this study, the window-modulated compounding (WMC) technique was integrated into three-dimensional (3D) ultrasound Nakagami imaging for improving the spatial visualization of backscatter statistics. A 3D WMC Nakagami image was produced by summing and averaging a number of 3D Nakagami images (number of frames denoted as N) formed using sliding cubes with varying side lengths ranging from 1 to N times the transducer pulse. To evaluate the performance of the proposed 3D WMC Nakagami imaging method, agar phantoms with scatterer concentrations ranging from 2 to 64 scatterers/mm were made, and six stages of fatty liver (zero, one, two, four, six, and eight weeks) were induced in rats by methionine-choline-deficient diets (three rats for each stage, total n = 18). A mechanical scanning system with a 5-MHz focused single-element transducer was used for ultrasound radiofrequency data acquisition. The experimental results showed that 3D WMC Nakagami imaging was able to characterize different scatterer concentrations. Backscatter statistics were visualized with various numbers of frames; N = 5 reduced the estimation error of 3D WMC Nakagami imaging in visualizing the backscatter statistics. Compared with conventional 3D Nakagami imaging, 3D WMC Nakagami imaging improved the image smoothness without significant image resolution degradation, and it can thus be used for describing different stages of fatty liver in rats.

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Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques

Cited by 12 publications

References 53 publications

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Hierarchical method for cataract grading based on retinal images using improved Haar wavelet

Three-dimensional Visualization of Ultrasound Backscatter Statistics by Window-modulated Compounding Nakagami Imaging

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