A Multi-Objective Enhancement Technique for Poor Contrast Magnetic Resonance Images of Brain Glioblastomas

Chandrashekar, Leena; Sreedevi, A.

doi:10.1016/j.procs.2020.04.190

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…The datasets chosen belong to Group I representing large tumors and Group II with small tumors. In 3 shows the expressions for various performance parameters, the detailed definitions are discussed in [30][31][32][33]. The proposed enhancement technique provides high PSNR, SSIM, UIQI and a minimum MSE.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

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Section: Experiments and Resultsmentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

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“…Generalized histogram equalization im-proves the background contrast of the image, but also loses much information from interested image regions. To solve this issue, we applied Contrast Limited Adaptive Histogram Equalization (CLAHE) [16]. In this method, if any histogram bin is above the specified Contrast Limit( by default it is set to 40), those bins are clipped and distributed uniformly to each other before applying Histogram Equalization.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Morphological and Marker-based Watershed Method for Detection and Segmentation of Brain Tumor Regions

Faisal Hafeez,

Zobia Suhail,

Reyer Zwiggelaar

2023

NUST J. Eng. Sci.

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Brain tumor detection is a complex problem in medical image analysis. Brain tumor is an abnormal growth of brain cellsthat is usually detected by Magnetic Resonance Images (MRI). In this paper, we propose an efficient algorithm for detecting brain tumors using MRI without skull removal. After applying basic image preprocessing techniques, morphological operations are used to detect the boundaries and sharpen the regions.Segmentation is performed using Otsu thresholding and then a marker watershed technique is used for final brain tumor segmentation. The proposed approach is evaluated on 3000 images from Brain Tumor Image Segmentation Challenge (BRATS) dataset. Experiments showed that with appropriate preprocessing and appropriate thresholding, good segmentation results can be achieved.

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“…Red is multiplied by 0.2989, green is multiplied by 0.587, and blue is multiplied by 0.1141. 𝐺′ = 0.2989 * 𝑅 + 0.587 * 𝐺 + 0.1141 * 𝐵 (10) Image enhancement improves the image [33] before processing [34]. Image enhancement in this study uses a combination of Histogram Equalization (HE) and Contrast Limited Histogram Equalization (CLAHE) [35].…”

Section: Image Grayscaling and Enhancementmentioning

confidence: 99%

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

Saifullah

Dreżewski

Khaliduzzaman

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a nondestructive quantitative study on embryos and their morphology in a precision poultry production system in the future.

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A Multi-Objective Enhancement Technique for Poor Contrast Magnetic Resonance Images of Brain Glioblastomas

Cited by 9 publications

References 13 publications

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Morphological and Marker-based Watershed Method for Detection and Segmentation of Brain Tumor Regions

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

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