Endometrium estimation in a sequence of ultrasonic images

Yang, Ching‐Chieh; Chung, Pau‐Choo; Tsai, Yao‐Chou

doi:10.1016/s0895-6111(00)00081-1

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In contrast to previous publications, our study developed, for the first time, a new software to segment the whole endometrium. Therefore, a reliable estimation of the entire endometrium can be obtained [15]. Since all estimations were directly performed by a computer, human error can be minimized.…”

Section: Resultsmentioning

confidence: 99%

Endometrium characterization in connection with uterine contraction

Yang

Chung

Tsai

et al. 2001

IEEE Trans. Nucl. Sci.

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The features of the endometrium are considered important factors in infertility treatment. This paper presents a new approach for estimating the endometrium based on a multiple threshold technique and a general mathematical method. The approach exploits gray-level information to organize and constrain the interpretation of the motion object by a recursive model. The recursive model is employed for threshold optimization of motion information segmentation. This is implemented by repeatedly shrinking the dimension of the maximum potential range of motion with an adaptive threshold throughout the whole process. The values to be estimated comprise motion frequency and thickness. The former is determined by change detection of the number of frame based changes in a sequence of images over a given time period with an appropriate sampling frequency. This can be achieved by a double-increment-of-sampling-frequency mechanism. The latter is the result of the motion object's dimension divided by its extended length. Experimental results indicate that it is possible to accurately estimate the thickness and motion frequency of the endometrium with the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

Endometrium characterization in connection with uterine contraction

Yang

Chung

Tsai

et al. 2001

IEEE Trans. Nucl. Sci.

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“…in addition to the sole measurement of endometrial thickness [4][5][6], To the authors' knowledge, there are no reported studies assessing quantitatively endometrial texture for differentiating malignant from benign endometrial tissue. Computerized analysis of endometrial tissue (in terms of histogram based features) has only implemented in the context of ameliorating in vitro fertilization (IVF) outcome [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography

Karahaliou

Skiadopoulos

Michail

et al.

IFIP International Federation for Information Processing

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Computer-aided classification of benign and malignant endometrial tissue, as depicted in 2D gray scale transvaginal ultrasonography (TVS), was attempted by computing texture-based features. 65 TVS endometrial images were collected (15 malignant, 50 benign) and processed with a wavelet based enhancement technique. Two regions of interest (ROIs) were identified (endometrium, endometrium margin) on each processed image. Thirty-two textural features were extracted from each ROI employing first and second order statistics texture analysis algorithms. Textural feature-based models were generated for differentiating benign from malignant endometrial tissue employing stepwise logistic regression analysis. Models' performance was evaluated by means of receiver operating characteristics (ROC) analysis. The best benign versus malignant classification was obtained from the model combining three textural features from endometrium and four textural features from endometrium margin, with corresponding area under ROC curve (Az) 0.956.

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Endometrium estimation in a sequence of ultrasonic images

Cited by 2 publications

References 17 publications

Endometrium characterization in connection with uterine contraction

Endometrium characterization in connection with uterine contraction

Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography

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