Multi-atlas-based automatic 3D segmentation for prostate brachytherapy in transrectal ultrasound images

2013 ◽  
Author(s):  
Saman Nouranian ◽  
S. Sara Mahdavi ◽  
Ingrid Spadinger ◽  
William J. Morris ◽  
S. E. Salcudean ◽  
...  
Keyword(s):  
Transrectal Ultrasound ◽  
Prostate Brachytherapy ◽  
Ultrasound Images ◽  
3D Segmentation

Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy

2016 ◽  
Vol 35 (3) ◽  
pp. 921-932 ◽  
Author(s):  
Saman Nouranian ◽  
Mahdi Ramezani ◽  
Ingrid Spadinger ◽  
William J. Morris ◽  
Septimu E. Salcudean ◽  
...  
Keyword(s):  
Transrectal Ultrasound ◽  
Prostate Brachytherapy ◽  
Ultrasound Images

Quantifying 125 I placement accuracy in prostate brachytherapy using postimplant transrectal ultrasound images

Brachytherapy ◽  
2017 ◽  
Vol 16 (2) ◽  
pp. 306-312 ◽  
Author(s):  
Muhammad F. Jamaluddin ◽  
Sunita Ghosh ◽  
Michael P. Waine ◽  
Ronald S. Sloboda ◽  
Mahdi Tavakoli ◽  
...  
Keyword(s):  
Transrectal Ultrasound ◽  
Prostate Brachytherapy ◽  
Ultrasound Images ◽  
Placement Accuracy

scholarly journals Quantifying Iodine-125 Placement Accuracy in Prostate Brachytherapy Using Post-Implant Transrectal Ultrasound Images

Brachytherapy ◽  
2016 ◽  
Vol 15 ◽  
pp. S180 ◽  
Author(s):  
Muhammad F. Jamaluddin ◽  
Sunita Ghosh ◽  
Michael Waine ◽  
Ronald S. Sloboda ◽  
Mahdi Tavakoli ◽  
...  
Keyword(s):  
Transrectal Ultrasound ◽  
Prostate Brachytherapy ◽  
Ultrasound Images ◽  
Placement Accuracy ◽  
Iodine 125

Prostate volume measurement by transrectal ultrasound and computed tomography before and after permanent prostate brachytherapy

2004 ◽  
Vol 60 (3) ◽  
pp. 767-776 ◽  
Author(s):  
Matthew C. Solhjem ◽  
Brian J. Davis ◽  
Thomas M. Pisansky ◽  
Torrence M. Wilson ◽  
Lance A. Mynderse ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Prostate Volume ◽  
Transrectal Ultrasound ◽  
Volume Measurement ◽  
Prostate Brachytherapy ◽  
Before And After

scholarly journals A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

2021 ◽  
Author(s):  
Rasa Vafaie
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Computational Time ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Initial Model ◽  
Hybrid Segmentation

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

3D segmentation of breast tumor in ultrasound images

2003 ◽  
Author(s):  
Jong In Kwak ◽  
Mal Nam Jung ◽  
Sang Hyun Kim ◽  
Nam Chul Kim
Keyword(s):  
Breast Tumor ◽  
Ultrasound Images ◽  
3D Segmentation

Impact of transrectal ultrasound- and computed tomography-based seed localization on postimplant dosimetry in prostate brachytherapy

Brachytherapy ◽  
2009 ◽  
Vol 8 (2) ◽  
pp. 255-264 ◽  
Author(s):  
Meng-Sang Chew ◽  
Jinyu Xue ◽  
Chris Houser ◽  
Vladimir Misic ◽  
Junsheng Cao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Transrectal Ultrasound ◽  
Prostate Brachytherapy ◽  
Postimplant Dosimetry ◽  
Seed Localization

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

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaofu Huang ◽  
Ming Chen ◽  
Peizhong Liu ◽  
Yongzhao Du
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Markov Random Fields ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
Texture Features ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Tumours ◽  
Data Set

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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