A novel contour detection method

<p>A biomimetic vision computing model based on multi-level feature channel optimization coding is proposed and applied to image contour detection, combining the end-to-end detection method of full convolutional neural network and the traditional contour detection method based on biological vision mechanism. Considering the effectiveness of the Gabor filter in perceiving the scale and direction of the image target, the Gabor filter is introduced to simulate the multi-level feature response on the visual path. The optimal scale and direction of the Gabor filter are obtained based on the similarity index, and they are used as the frequency separation parameter of the NSCT transform. The contour sub-image obtained by the NSCT transform is combined with the original image for feature enhancement and fusion to realize the primary contour response. The low-dimensional and low-redundancy primary contour response is used as the input sample of the network model to relieve network pressure and reduce computational complexity. A fully improved convolutional neural network model is constructed for multi-scale training, through feature encoder to feature decoder, to achieve end-to-end pixel prediction, and obtain a complete and continuous detection image of the subject contour. Using the BSDS500 atlas as the experimental sample, the average accuracy index is 0.85, which runs on the device CPU at a detection rate of 20+ FPS to achieve a good balance between training efficiency and detection effect.</p>

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An improved contour detection method based on level set and watershed transform

10.1117/12.832902 ◽

2009 ◽

Author(s):

Shuang Wang ◽

Shenggao Fu ◽

Licheng Jiao ◽

Xiaojing Zhang

Keyword(s):

Level Set ◽

Detection Method ◽

Contour Detection ◽

Watershed Transform

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A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images

Sensors ◽

10.3390/s21124095 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4095

Author(s):

Xiaoqi Cheng ◽

Junhua Sun ◽

Fuqiang Zhou

Keyword(s):

Detection Method ◽

Three Dimensional ◽

Contour Detection ◽

Two Dimensional ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Complex Scenes ◽

Dimensional Reconstruction ◽

Overall Evaluation ◽

Evaluation Metric

The tube contours in two-dimensional images are important cues for optical three-dimensional reconstruction. Aiming at the practical problems encountered in the application of tube contour detection under complex background, a fully convolutional network (FCN)-based tube contour detection method is proposed. Multi-exposure (ME) images are captured as the input of FCN in order to get information of tube contours in different dynamic ranges, and the U-Net type architecture is adopted by the FCN to achieve pixel-level dense classification. In addition, we propose a new loss function that can help eliminate the adverse effects caused by the positional deviation and jagged morphology of tube contour labels. Finally, we introduce a new dataset called multi-exposure tube contour dataset (METCD) and a new evaluation metric called dilate inaccuracy at optimal dataset scale (DIA-ODS) to reach an overall evaluation of our proposed method. The experimental results show that the proposed method can effectively improve the integrity and accuracy of tube contour detection in complex scenes.

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263. Effect of collimator on Fully Automated Left Ventricular Contour Detection Method

Japanese Journal of Radiological Technology ◽

10.6009/jjrt.kj00003533793 ◽

1992 ◽

Vol 48 (3) ◽

pp. 528

Keyword(s):

Detection Method ◽

Left Ventricular ◽

Contour Detection

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LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection

10.54294/xvoael ◽

2009 ◽

Author(s):

Jeroen Wijnhout ◽

Dennis Hendriksen ◽

Hans Van Assen ◽

Rob Van der geest

Keyword(s):

Detection Method ◽

User Interaction ◽

Clinical Applications ◽

Automated Detection ◽

Contour Detection ◽

Data Sets ◽

3D Segmentation ◽

Endocardial Contour ◽

Ventricle Segmentation ◽

Myocardial Contour

In this paper a contour detection method is described and evaluated on the evaluation data sets of the Cardiac MR Left Ventricle Segmentation Challenge as part of MICCAI 2009’s 3D Segmentation Challenge for Clinical Applications. The proposed method, using 2D AAM and 3D ASM, performs a fully automated detection of the myocardial contours, not requiring any user interaction. The algorithm’s performance is reported using the metrics provided by the LV Challenge organization. Endocardial contour detection was classified as successful in 86% of the images and epicardial contours in 94%. The average perpendicular distance (APD) of the successful contours was 2.28 mm and 2.29 mm for the endo- and epicardial contours, respectively.

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