MRI image preprocessing and noise removal technique using linear and nonlinear filters

Suhas, S.; Venugopal, C. R.

doi:10.1109/iceeccot.2017.8284595

Cited by 51 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For instance, each image can be zoomed out, have unnecessary pixel data, or contain noise. A type of preprocessing and noise removal technique with MRI images [7] was used using different types of filters to get accurate observations.…”

Section: B Preprocessing and Data Augmentationmentioning

confidence: 99%

State Classification of Cooking Objects Using a VGG CNN

Mott¹

2019

State Recognition Symposium

View full text Add to dashboard Cite

In machine learning, it is very important for a robot to know the state of an object and recognize particular desired states. This is an image classification problem that can be solved using a convolutional neural network. In this paper, we will discuss the use of a VGG convolutional neural network to recognize those states of cooking objects. We will discuss the uses of activation functions, optimizers, data augmentation, layer additions, and other different versions of architectures. The results of this paper will be used to identify alternatives to the VGG convolutional neural network to improve accuracy.

show abstract

Section: B Preprocessing and Data Augmentationmentioning

confidence: 99%

State Classification of Cooking Objects Using a VGG CNN

Mott¹

2019

State Recognition Symposium

View full text Add to dashboard Cite

show abstract

“…Image de-noising is an important task in any type of image processing. The main aim of any de-noise model is that it should preserve the edges while remove noise as far as possible [20]. We apply the median filter, which is a nonlinear filter, to the image.…”

Section: Ga Clustering Algorithmmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Combination of K-Means Clustering and Genetic Algorithm

2018

View full text Add to dashboard Cite

Introduction: Medical image processing aimed at reducing human error rates attracted many researchers. The Segmentation of magnetic resonance image for tumor detection is one of the recognized challenges in the treatment of the disease. Considering the importance of this issue in the present study, the diagnosis of brain tumor is considered.Material and methods: One of the most popular and most widely used methods in the field of segmentation of images of resonance imaging of the brain is the k-means clustering algorithm, which, despite the diagnosis of a tumor, fall in to local optimum problem, followed by a reduction in the accuracy of the diagnosis tumors are malignant. In this study, we aimed to solve this problem and subsequently increase the accuracy of diagnosis of malignant tumors, a GA-clustering combination of clustering based on k-means and genetic algorithms.Results: How to combine in the way that the genetic algorithm is applied to each repetition of the K-means algorithm and, by scanning more in the space of the answer, is trying to find higher quality cluster centers. The effectiveness of the proposed method has been investigated on a number of images of BRATS standard collections. It is also compared with the K-means algorithm.Conclusion: The results show that the proposed algorithm provides better results than the K-means algorithm.

show abstract

“…Before cracks in the captured image are identified, the image needs to be preprocessed because acquisition hardware and the natural lighting in the actual scene may inevitably introduce some interference and noise into the captured image [32][33][34]. In order to facilitate subsequent image processing, these unfavorable factors must be eliminated firstly.…”

Section: Image Preprocessingmentioning

confidence: 99%

Pavement Damage Crack Recognition Method Based on High-Resolution Linear Array Cameras and Adaptive Lifting Algorithm

Han¹,

Tu²,

Yang³

et al. 2018

Preprint

View full text Add to dashboard Cite

MRI image preprocessing and noise removal technique using linear and nonlinear filters

Cited by 51 publications

References 7 publications

State Classification of Cooking Objects Using a VGG CNN

State Classification of Cooking Objects Using a VGG CNN

Diagnosis of Brain Tumor Using Combination of K-Means Clustering and Genetic Algorithm

Pavement Damage Crack Recognition Method Based on High-Resolution Linear Array Cameras and Adaptive Lifting Algorithm

Contact Info

Product

Resources

About