Modified Histogram Equalization for Improved CNN Medical Image Segmentation

Saifullah, Shoffan; Dreżewski, Rafał

doi:10.1016/j.procs.2023.10.295

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…One avenue of research explores the enhancement of medical image segmentation through adaptive histogram equalization [50], highlighting its potential for contrast improvement [23,29,51]. Additionally, a comprehensive survey has delved into image enhancement techniques within medical imaging [52].…”

Section: Enhancement Techniquesmentioning

confidence: 99%

“…By adapting PSO to the task of medical image segmentation [27,28], we aim to exploit its ability to converge toward the most suitable image partitioning considering both global and local characteristics. Histogram equalization (HE) is a well-established image enhancement technique that adjusts the pixel intensities to achieve a more uniform distribution, thereby improving contrast and mitigating the effects of non-uniform illumination [29]. Integrating HE into our approach complements the optimization capabilities of PSO by preparing the input images for segmentation by enhancing the visibility of critical details within medical images.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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Section: Enhancement Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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“…The accurate segmentation of brain tumors from MRI scans plays a pivotal role in early diagnosis [2]. However, manual segmentation methods are often time-consuming and prone to error [3], making the development of automated and precise segmentation techniques essential [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Saifullah,

Dreżewski

2024

IJECE

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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

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Enhancing Breast Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification

Saifullah,

Dreżewski

2024

Lecture Notes in Computer Science

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Modified Histogram Equalization for Improved CNN Medical Image Segmentation

Cited by 6 publications

References 27 publications

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Enhancing Breast Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification

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