Automated localization and segmentation techniques for B-mode ultrasound images: A review

Meiburger, Kristen M.; Acharya, U. Rajendra; Molinari, Filippo

doi:10.1016/j.compbiomed.2017.11.018

Cited by 108 publications

(47 citation statements)

References 232 publications

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“…Anatomical imaging technique such as computed tomography (CT), which offers high accuracy in measuring the bladder volume, typically suffers the high radiation, rigorous measurement environment and inappropriateness for continuous monitoring [9]. Ultrasound has been widely employed in clinic to measure the volume of bladder due to its high portability and measurement accuracy [10–13]. Despite these benefits, the ultrasound measurement must be carried out by professionals and is not able to perform continuous monitoring, preventing its use as a routine clinical tool for bladder volume monitoring.…”

Section: Introductionmentioning

confidence: 99%

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance

Peng

Yang

et al. 2019

BioMed Eng OnLine

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PurposeThe aim of this study was to document more appropriate electrode location of a four-electrode-based electrical impedance technology in the monitoring of bladder filling, and to characterize the relationship between bladder filling duration and the measured electrical impedances.MethodsA simulation study, based on a 2-dimension computational model, was conducted to determine the preferable locations of excitation and measurement electrodes in a conventional four-electrode setup. A human observation study was subsequently performed on eight healthy volunteers during natural bladder urine accumulation to validate the result of the simulation study. The correlation between the bladder filling time and the measured electrical impedance values was evaluated.ResultsThe preferable location of measurement electrodes was successively validated by the model simulation study and human observation study. Result obtained via the selected electrodes location revealed a significant negative correlation (R = 0.916 ± 0.059, P < 0.001) between the measured electrical impedance and the urine accumulation time, which was consistent with the result of simulation study.ConclusionsThe findings in this study not only documented the desirable electrodes location to monitor the process of bladder urine accumulation using four-electrode measurement, but also validated the feasibility of utilizing electrical impedance technique to monitor and estimate the bladder urine volume for those with urological disorders.

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Section: Introductionmentioning

confidence: 99%

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance

Peng

Yang

et al. 2019

BioMed Eng OnLine

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“…In breast ultrasound images, the traditional breast tumor classification technology mainly includes the following four steps [ 22 , 23 ]: image preprocessing, image segmentation, feature extraction, and tumor classification. Among them, feature extraction is the main task of breast tumor classification, which has a great impact on the classification results [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Wei

et al. 2020

Computational and Mathematical Methods in Medicine

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The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

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“…This is because the area comprises of several body parts of the fetus, such as umbilical cord, placenta, uterus, and a less strict border between amniotic fluid and other parts. Therefore, the segmentation method is performed in order to obtain the amniotic fluid area on the Bmode ultrasound image, which has noise, blurry edge, artifact, and low contrast qualities [6]. Ultrasound images have relatively low contrast with lots of noise around the object or organ, which needs to be observed.…”

Section: Introductionmentioning

confidence: 99%

Pixel Classification Based on Local Gray Level Rectangle Window Sampling for Amniotic Fluid Segmentation

Ayu¹,

Hartati²

2021

IJIES

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This study analyses the use of a pixel classification model to segment amniotic fluid areas on ultrasound (US) images characterized by noise, blurry edge, artifacts, and low contrast. In contrast with the previous methods, this study constrains a training set of pixels based on neighbourhood information with the rectangle window sampling method used to determine the characteristics of each pixel in its environment specifically. The feature extraction is no longer based on the global characteristics of the object rather by taking the value of each pixel in the object area using the sampling window. This research also combines the local first-order statistical methods and gray level information in the window to obtain the characteristics of each pixel. Furthermore, Random Forest and Decision Tree (C.45) were used to classify each pixel into four classes, namely amniotic fluid, placenta, uterus, and fetal body. The classification performance testing of pixel sampling data showed that the Random forest with 5 × 7 window sizes achieved the highest performance at 99.5% accuracy, precision, and recall, respectively. Furthermore, the proposed model was evaluated using 50 new test US images to segment the amniotic fluid area. According to experimental result, proposed models can produce better segmentation area with an increase in the IoU value by 18.3% or a Jaccard coefficient value rate of 0.183 in the range of 0-1 with the previous state of the art method. Furthermore, the proposed model reduces the error rate and improves accuracy by 6.61% and 84.77%, respectively.

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Automated localization and segmentation techniques for B-mode ultrasound images: A review

Cited by 108 publications

References 232 publications

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance

A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Pixel Classification Based on Local Gray Level Rectangle Window Sampling for Amniotic Fluid Segmentation

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