Multi-focus image fusion for visual sensor networks in DCT domain

Haghighat, Mohammad; Aghagolzadeh, Ali; Seyedarabi, Hadi

doi:10.1016/j.compeleceng.2011.04.016

Cited by 223 publications

(119 citation statements)

References 10 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…Otherwise, if a pixel within the tumor area is segmented incorrectly as healthy and is surrounded by pixels that are segmented as tumor, then the pixel is changed to a pathological pixel. If the window size of the applied filter is increased, the quality of the output image significantly augments at the expense of complexity increase [40]. Herein, consistency verification was applied in a 5 × 5 neighborhood window [11].…”

Section: Tumor Segmentation Resultsmentioning

confidence: 99%

“…Tables 2 and 3 Under this segmentation process, some brain tumor parts in the MRI slices were incorrectly segmented as healthy tissues, and some healthy brain tissues were incorrectly segmented as pathological tissues. To eliminate these ambiguities and reduce the FPs and FNs, a consistency verification algorithm was applied for post-processing [11,40]. The majority filter was adopted to remove the FPs and FNs by replacing the segmented pixels inconsistent with their neighboring segmented pixels in a certain neighborhood.…”

Section: Tumor Segmentation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

et al. 2016

View full text Add to dashboard Cite

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% ± 4.7% compared with manual processes.

show abstract

Section: Tumor Segmentation Resultsmentioning

confidence: 99%

Section: Tumor Segmentation Resultsmentioning

confidence: 99%

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

et al. 2016

View full text Add to dashboard Cite

show abstract

“…1 The prevailing thought is that the resulting image will be more informative than any of the input images. 2 The multiple input images can be multimodal, multifocus, or multiview images.…”

Section: What Is Image Fusion?mentioning

confidence: 99%

Multispectral Image Fusion and Colorization

Blasch

Zheng

Liu

2018

View full text Add to dashboard Cite

“…As of late, a few image combination strategies in DCT area have been proposed. T. Jinshan [74] proposed two techniques in DCT area yet these strategies endure some undesirable symptoms like obscure like not clear image or blocking residue which corrupt the image quality. The calculation proposed in [75] called Max prompts choosing a wrong JPEG coded pieces in light of the fact that the quantity of higher esteemed AC coefficients is an invalid rule when the greater part of the AC coefficients are quantized to zeros amid the quantization.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Medical Image Segmentation Methods with Different Modalitites

Sumithra¹,

Malathi²

2018

IJERT

View full text Add to dashboard Cite

-This paper shows a review on the offered methods for segmentation of brain tumor magnetic resonance imaging (MRI) and computed tomography (CT) images. Segmentation of brain MRI and CT images are broadly used as a preprocessing, for research that inhabit study and mechanization, in the domain of medical image processing. MRI and CT images segmentation is difficult job because of the same characteristics between hard and soft group of muscle anatomy in the brain image. Also the many parts of identical space present in an image differentiate with the image partition and direction. The selection of an suitable type for segmentation that is build up on the image featue. This review has been done in the point of view of authorize the mixture of a segmentation type for MRI and CT images. This review is differentiated based on the methods processed in segmentation.

show abstract

Multi-focus image fusion for visual sensor networks in DCT domain

Cited by 223 publications

References 10 publications

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

Multispectral Image Fusion and Colorization

A Survey on Medical Image Segmentation Methods with Different Modalitites

Contact Info

Product

Resources

About