Modified transform‐based gamma correction for <scp>MRI</scp> tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Kollem, Sreedhar; Reddy, Katta Rama Linga; Rao, D. Srinivasa

doi:10.1002/ima.22429

Cited by 25 publications

(5 citation statements)

References 20 publications

(25 reference statements)

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“…The ROI is clearly visible in entropy filtered image, however, to measure the size of the region needs more processing. At this point, gamma correction is applied to enhance the dark and light intensity of the image and improves the brightness [27]. It performs nonlinear methods to every pixel.…”

Section: Methodsmentioning

confidence: 99%

Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image

George¹,

Bhimasena²

2022

IJC

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Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients

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Section: Methodsmentioning

confidence: 99%

Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image

George¹,

Bhimasena²

2022

IJC

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“…For a 512x512 image, choose a grid size of 32x32 or 64x64, and select multiple thread blocks for parallel processing according to the size of the image and the complexity of the processing task. For a 512x512 image, select a 64x64 grid [12][13], the size of each thread block is 16x16, a total of 4096 thread blocks.Use the shared memory configuration template (__shared__) provided by CUDA to declare the size of the shared memory. This article is set to __shared__ float shared_data [16][16].Table 1 shows the number of thread blocks and corresponding grids:…”

Section: Grid Designmentioning

confidence: 99%

Optimization and acceleration of image processing algorithms based on CUDA parallel architecture

Song

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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How to solve the problem of excessive execution time of current image processing algorithms? In this research, the CUDA parallel architecture is used as the basis to optimize and accelerate the image processing algorithm. By rationally designing parallel algorithms, optimizing memory access and parallel task allocation, and using shared memory and parallel computing capabilities, the efficiency of image processing algorithms can be maximized. The experimental results show that the execution time of the method in this paper requires a maximum of 6s and a minimum of 1s. The optimization and acceleration of the image processing algorithm based on the CUDA parallel architecture have achieved remarkable results. Compared with traditional serial algorithms, the speed of image processing is greatly improved through parallel computing and the optimization of shared memory. At the same time, through reasonable thread block and grid design, the memory access mode and parallel task allocation are optimized, and the performance and efficiency of the algorithm are further improved.

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“…Ehrhardt et al [5] proposed a sophisticated approach that combines intensity, texture, and Gradient Vector Flow, providing distinct tumor boundaries while preserving essential details. Meanwhile, Kollem et al [6] achieved significant noise reduction using Haar & Daubechies Transforms, elevating the quality of medical images, particularly for speckle noise reduction. Further advancements emerged with Orea-Flores et al [7], who masterfully integrated denoising and resolution development techniques, presenting a formidable strategy for improving overall image quality.…”

Section: Literature Surveymentioning

confidence: 99%

“…Therefore, there is a pressing need for robust and automated brain tumor segmentation methods that can significantly impact the diagnosis and treatment of brain tumors [5]. Such advancements could also lead to early detection and treatment of conditions like Alzheimer's disease (AD), schizophrenia, and dementia [6]. Automating brain tumor segmentation can aid radiologists in conveying critical information about tumor volume, location, and shape, thereby facilitating more effective and meaningful treatment decisions [7].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel De-Noising Approach for Precision Edge-Based Fragmentation in MRI Brain Tumor Segmentation

Chaitanya,

Satpathy

2023

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Brain tumors, the second leading cause of mortality as identified by numerous health agencies, constitute a significant health challenge. Given the integral role of the brain in governing essential functionalities, such as memory, vision, learning, and problem-solving, early detection of malignant brain tumors is critical for effective medical intervention. Magnetic Resonance Imaging (MRI), demonstrating superior precision and reliability over Computed Tomography (CT), is a preferred modality for brain cancer identification. This study introduces an innovative approach to brain tumor detection and segmentation, utilizing fragmentation. Fragmentation, a promising method for brain cancer analysis, involves the differentiation of cancerous tissue from other brain components, such as fatty tissue, edema, normal brain matter, and cerebrospinal fluid. The critical role of denoising in this process, which entails iterative thresholding until the tumor is appropriately segregated, is examined. Further, the use of image classification for distinguishing abnormal regions indicative of a brain tumor in MRI scans is outlined. The proposed Multilevel De-noising model with Precision Edge-based Fragmentation for Tumor Size Diagnosis (MD-PES-TSD) is presented as a comprehensive framework for the detection and segmentation of MRI images. The MD-PES-TSD model is designed to effectively reduce image noise, identify structural brain edges, differentiate between abnormal and normal brain regions, and ultimately determine the size of the tumor. An evaluation of MRI data scans segregated into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) regions, following the implementation of early-stage denoising in the pre-processing phase and feature extraction through segmentation, is conducted. The MD-PES-TSD model is shown to outperform existing models in comparative analysis, signifying its potential as an effective solution for brain tumor detection and segmentation.

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Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Cited by 25 publications

References 20 publications

Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image

Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image

Optimization and acceleration of image processing algorithms based on CUDA parallel architecture

A Multilevel De-Noising Approach for Precision Edge-Based Fragmentation in MRI Brain Tumor Segmentation

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