Computer-aided analysis of transrectal ultrasound images of the prostate

Gómez-Ferrer, Á.; Arlandis, S.

doi:10.1016/j.acuroe.2011.02.004

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Therefore, TRUS has poor visual interpretation and cannot accurately identify the tumour area. Gomez-Ferrer and Arlandis [ 25 ] found only 12.6% hypoechoic lesions in their work, which were found in most (68%) benign tissues. These data confirm the need to try to analyze transrectal ultrasound images with computer assistance.…”

Section: Discussionmentioning

confidence: 86%

“…However, this research method is only suitable for use when data are scarce. Gomez-Ferrer and Arlandis [ 25 ] recorded 288 cases of transrectal ultrasound-guided biopsy and extracted three still images from the biopsy area. The texture features of ultrasonic images are obtained by “simple mapping” on grey and spatial grey correlation matrices.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

Huang

Chen

Liu

et al. 2020

Computational and Mathematical Methods in Medicine

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Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

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Section: Discussionmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

Huang

Chen

Liu

et al. 2020

Computational and Mathematical Methods in Medicine

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Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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show abstract

Computer-aided analysis of transrectal ultrasound images of the prostate

Cited by 2 publications

References 23 publications

Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

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