Image segmentation with pulse-coupled neural network and Canny operators

Jiang, Wen; Zhou, Hao; Shen, Yue; Liu, Bo; Fu, Zi-Gang

doi:10.1016/j.compeleceng.2015.03.028

Cited by 22 publications

(5 citation statements)

References 22 publications

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“…The abovementioned model needs a proportionately high-performance computer concerning the number of intermediate layers for segmentation. A hybrid approach with a combination of the pulse-coded neural network (PCNN) and feed-forward back neural network (FFBNN) [ 35 , 36 ] technique identifies the preliminary seed points. The latter approach would setback the points that would stabilize the input, leading to an optimal image segmentation level.…”

Section: Literature Reviewmentioning

confidence: 99%

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Srinivasu

Krishna²,

Ahmed

et al. 2023

Journal of Healthcare Engineering

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Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist’s task and the practitioners more comfortable assessing the tumor’s progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

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Section: Literature Reviewmentioning

confidence: 99%

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Srinivasu

Krishna²,

Ahmed

et al. 2023

Journal of Healthcare Engineering

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“…Singh et al applied the Sobel operator to detect edges in real-time surveillance videos and presented a new resource-efficient FPGA-based hardware architecture [ 13 ]. Jiang et al performed color image segmentation using the Canny operator by combining it with a pulse-coupled neural network [ 14 ]. Based on the color vector angle and the Canny operator, Tang et al proposed a robust image hashing mechanism to achieve a desirable tradeoff of classification performances between rotation robustness and discrimination [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

Cao

Chen

Wang

et al. 2018

Computational Intelligence and Neuroscience

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The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

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“…So the output of each neuron has two statuses: pulse (1 status) and non-pulse (0 status), and the output statuses of neurons comprise a binary image. The binary image represents the features of the input image [7,[10][11][12]. Fig.…”

confidence: 99%

“…In recent years, PCNN model is getting more and more attention. It has been widely used in image processing and other fields, and shows extremely superior performances in these fields [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Block medical image fusion based on adaptive PCNN

Yang

Jin

Zhou

2015

2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)

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We proposed an effective block medical image fusion method based on adaptive pulse coupled neural networks (PCNN) in this paper. Source images are divided into several blocks, and then we calculate the spatial frequency (SF) of the blocks as linking strength β of the PCNN, so it adjusts β of the PCNN adaptively. The block images are input into PCNN to get the oscillation frequency graph (OFG), which expresses the quality of the block images, so we can fuse the clear part of the source images. The experimental results show that the block medical image fusion algorithm is more efficient than other common image fusion algorithms, and prove the adaptive PCNN method is effectively as well. Keywords-Simplified pulse coupled neural networks; oscillation frequency graph; medical image; spatial frequenc; block image fusionI.

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Image segmentation with pulse-coupled neural network and Canny operators

Cited by 22 publications

References 22 publications

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

Block medical image fusion based on adaptive PCNN

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