A Review on Various Edge Detection Techniques in Distorted Images

Jain, Geetanjali; Kaur, Supreet

doi:10.23956/ijarcsse/v7i4/0234

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The method finds the rate of change in grey intensity and then detects the local maxima in the gradient magnitude. It locates the centres of thick edges and the localization of edges is good [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

2021

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Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

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Section: Introductionmentioning

confidence: 99%

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

2021

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Res BCDU-SCB Net: A Lung CT Image Segmentation based on SC-BLSTM

Wang,

Li,

Tang

et al. 2023

Proceedings of the 2023 8th International Conference on Biomedical Signal and Image Processing

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ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Jalali

Fateh

Rezvani

et al. 2021

Sensors

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Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

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A Review on Various Edge Detection Techniques in Distorted Images

Cited by 3 publications

References 9 publications

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

Res BCDU-SCB Net: A Lung CT Image Segmentation based on SC-BLSTM

ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

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