MR Brain Image Enhancement via Learning Ensemble

Wang, Xiaoyan; An, Zhenzhou; Wang, Haifeng; Chang, Yuchou

doi:10.1109/iisr.2018.8535649

Cited by 3 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, there are different approaches that categorize image enhancement methods, distinguishing in Histogram Equalization, Retinex, the Dehazing Model, and Deep Learning (DL). [18] and [19] on the other hand share the approaches in Global Enhancement -consisting for example of Linear Amplifying and Histogram Equalizationand Local Enhancement -among others consisting of Deep Neural Networks and Retinex. [20] speaks however of direct and indirect methods.…”

Section: Resultsmentioning

confidence: 99%

“…[21] distinguishes between model-based, fusion-based, and learning-based approaches. [19] and [2] divide into the spatial domain Approach -which stands for the direct manipulation of the pixels -and the Frequency Domain method -which makes use of the Fourier transform, for example.…”

Section: Resultsmentioning

confidence: 99%

“…Equalization (HE) is the change of the grayscale histogram of the original image from a relative grayscale interval to a uniform distribution over the entire grayscale spectrum [19,24]. Each pixel receives a grayscale value in the domain [0, 1] [22].…”

Section: Histogram Equalization (He) Histogrammentioning

confidence: 99%

“…Each pixel receives a grayscale value in the domain [0, 1] [22]. The goal of HE is to maximize the amount of information in an image by improving contrast [19,24]. Therefore, HE is applied to images with low contrast, e.g.…”

Section: Histogram Equalization (He) Histogrammentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Klaiber¹,

Klopfer²

2022

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our results

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Histogram Equalization (He) Histogrammentioning

confidence: 99%

Section: Histogram Equalization (He) Histogrammentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Klaiber¹,

Klopfer²

2022

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

show abstract

“…As MR brain images have low contrast histogram equalization can be used to enhance the quality of the images [6]. So, to improve the resolution of the image histogram processing is implemented.…”

Section: Introductionmentioning

confidence: 99%

Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

Angalakurthi

Ramamurthy²

2021

Braz. arch. biol. technol.

View full text Add to dashboard Cite

The proficiency of image processing is of extreme importance in perceiving and collecting information from the images, which includes the process of changing or interpreting existing images. In medical image processing, imaging with more accuracy plays a crucial role in better diagnosis or for the posterior analysis of treatment. Magnetic Resonance Imaging (MRI) is a medicinal creative tool for studying the internal structures and functionalities of human brain, knee, heart, liver, etc. Typical MR scans are essential now for better diagnosis but, limited resolution that is often inadequate for extracting detailed and reliable information. So, for the super resolution (SR) of MR brain images concepts of compressive sensing (CS) & fuzzy logical rules to improve data quality are proposed in this paper. Usually, reconstruction of an SR image is the formation of high resolution (HR) image which is obtained from one or few low resolution (LR) images. In the proposed method, with the help of compressive sensing a very limited number of images are considered even though it's a challenging task and fuzzy logical rules for a specific membership function are applied to improve the resolution of the image. To assess the performance of the proposal, different metrics are evaluated and achieved better results.

show abstract

A Multi-view Learning Approach for Glioblastoma Image Contrast Enhancement

Wang

Zhou

et al. 2020

Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology

View full text Add to dashboard Cite

MR Brain Image Enhancement via Learning Ensemble

Cited by 3 publications

References 7 publications

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

A Multi-view Learning Approach for Glioblastoma Image Contrast Enhancement

Contact Info

Product

Resources

About