Realizations of fast 2-D/3-D image filtering and enhancement

Tang, Haiying; Zhuang, Ting; Wu, Ed X.

doi:10.1109/42.913179

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…Enhancing the region boundary could improve tumor detection performance. 38 The stick filter 30,31 was used to enhance the contrast of the edges for extracting the boundary information. In this study, a stick with a size of 7 ×7 square area was adopted to enhance the edge information, and 12 templates with short lines of possible orientations were adopted in the stick processing.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Shen

Huang

et al. 2013

Ultrason Imaging

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Automated whole breast ultrasound (ABUS) has become a popular screening tool in recent years. To reduce the review time and misdetection from ABUS images by physicians, a computer-aided detection (CADe) system for ABUS images based on a multiview method is proposed in this study. A total of 58 pathology-proven lesions from 41 patients were used to evaluate the performance of the system. In the proposed CADe system, the fuzzy c-mean clustering method was applied to detect tumor candidates from these ABUS images. Subsequently, the tumor likelihoods of these candidates could be estimated by a logistic linear regression model based on the intensity, morphology, location, and size features in the transverse, longitudinal, and coronal views. Finally, the multiview tumor likelihoods of the tumor candidates could be obtained from the estimated tumor likelihoods of the three views, and the tumor candidates with high multiview tumor likelihoods were regarded as the detected tumors in the proposed system. The sensitivities of the multiview tumor detection for selecting 5, 10, 20, and 30 tumor candidates with the largest multiview tumor likelihoods were 79.31%, 86.21%, 96.55%, and 98.28%, respectively.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Shen

Huang

et al. 2013

Ultrason Imaging

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“…To alleviate the edge loss and blurring produced by noise filtering, the Butterworth filter is further employed for image sharpening [62,63]. The system function of an n-order Butterworth high-pass filter is defined as follows:…”

Section: Enhancement Of Frft Spectrummentioning

confidence: 99%

Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

Liu

et al. 2021

Remote Sensing

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The parametric decomposition of full-waveform Lidar data is challenging when faced with heavy noise scenarios. In this paper, we report a fractional Fourier transform (FRFT)-based approach for accurate parametric decomposition of pulsed Lidar signals with noise corruption. In comparison with other joint time-frequency analysis (JTFA) techniques, FRFT is found to present a one-dimensional Lidar signal by a particular two-dimensional spectrum, which can exhibit the mathematical distribution of the multiple components in Lidar signals even with a heavy noise interference. A FRFT spectrum-processing solution with histogram clustering and moving LSM fitting is designed to extract the amplitude, time offset, and pulse width contained in the mathematical distribution. Extensive experimental results demonstrate that the proposed FRFT spectrum analysis method can remarkably outperform the conventional Levenberg–Marquardt-based method. In particular, it can accurately decompose the amplitudes, time offsets, and pulse widths of the pulsed Lidar signal with a −10-dB signal-to-noise-ratio by mean deviation ratios of 4.885%, 0.531%, and 7.802%, respectively.

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“…Given the fact that different regions have different intensities in the ultrasound image, the boundary of the thermal lesion was accentuated by mapping the intensities, based on a sigmoid intensity transformation [10]. Sigmoid transformation is a nonlinear mapping of a specific range of intensities in a new range of intensities with smooth and continuous transitions.…”

Section: Real-time Acquisitionmentioning

confidence: 99%

Characterization of ultrasound images of HIFU-induced lesions by extraction of its morphological properties

Márquez

Leija

Vera

2010

2010 7th International Conference on Electrical Engineering Computing Science and Automatic Control

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The purpose of this work is to compute several image processing techniques, applied to US images of HIFUinduced lesions, to characterize the shape, contour, position and the orientation of thermal lesions. In order to obtain realtime monitoring of HIFU treatment, a B-mode US imaging system and HIFU were synchronized. First, the HIFU sonication procedure consisted of applying an initial power of 10 W over an 8% BSA tissue-mimicking polyacrylamide gel until a hyperechoic spot appeared. Afterwards, the power was gradually increased up to 40 W for 4 min. The frame acquisition rate was set to 5 fps. Then, the region of interest (ROI) was manually selected and an image segmentation algorithm was implemented to isolate the thermal lesion based on different image pre-processing techniques and morphological operations. Thereafter, parameters such as area growing, the center of mass, eccentricity and the orientation of the equivalent ellipse were calculated to extract some morphological lesion features. This approach can be used as feedback system of the thermal lesion formation to determine more accurately the ablation zone.

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Realizations of fast 2-D/3-D image filtering and enhancement

Cited by 17 publications

References 10 publications

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

Characterization of ultrasound images of HIFU-induced lesions by extraction of its morphological properties

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