Mammogram Image Segmentation Using Susan Corner Detection

Kumar, Tarun; Kalaiselvi, K.; Velu, C. M.; Manivannan, Siyamalan; Babu, D. Vijendra

doi:10.1109/icesc51422.2021.9532863

Cited by 3 publications

(1 citation statement)

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“…Accordingly, choosing an appropriate corner detector, which can produce adequate edge feature points, is necessary. Among several famous corner detectors [ 6 , 7 , 8 , 9 ], the SUSAN detector has better robustness to noise and reduces the location error of edge feature points [ 10 ]. Most importantly, the SUSAN operator can extract corner points and edge points at the same time, which retains the contour information of images, to meet the visual characteristic of human eyes focusing on edge points.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

et al. 2023

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Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

et al. 2023

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Development and Application of Mechanical Design Engineering Database Based on Simulated Annealing Algorithm

Mohamed,

Alimjon,

Choriev

et al. 2023

2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon)

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Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model

Rogelio¹,

Dadios²,

Vicerra³

2022

JACIII

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The primary purpose of this research is to implement Deeplabv3 architecture’s deep neural network in detecting and segmenting portable X-ray source model parts such as body, handle, and aperture in the same color scheme scenario. Similarly, the aperture is smaller with lower resolution making deep convolutional neural networks more difficult to segment. As the input feature map diminishes as the net progresses, information about the aperture or the object on a smaller scale may be lost. It recommends using Deeplabv3 architecture to overcome this issue, as it is successful for semantic segmentation. Based on the experiment conducted, the average precision of the body, handle, and aperture of the portable X-ray source model are 91.75%, 20.41%, and 6.25%, respectively. Moreover, it indicates that detecting the “body” part has the highest average precision. In contrast, the detection of the “aperture” part has the lowest average precision. Likewise, the study found that using Deeplabv3 deep neural network architecture, detection, and segmentation of the portable X-ray source model was successful but needed improvement to increase the overall mean AP of 39.47%.

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Mammogram Image Segmentation Using Susan Corner Detection

Cited by 3 publications

References 10 publications

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

Development and Application of Mechanical Design Engineering Database Based on Simulated Annealing Algorithm

Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model

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