Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

Memon, Asif Aziz; Soomro, Shafiullah; Shahid, Muhammad Tanseef; Munir, Asad; Niaz, Asim; Choi, Kwang Nam

doi:10.1155/2020/6317415

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…From picture input to categorization prediction, users can expect a smooth process culminating in a clear "No brain tumor detected" or "Brain tumor identified." The predictive characteristic, offering a high degree of accuracy, holds great potential for enhancing medical diagnostics and stands as a cornerstone of our findings [27].…”

Section: Image Processing and Predictive Precisionmentioning

confidence: 85%

An Efficient GPU/Deep Learning Model Approach for Brain Tumor Detection in Pakistan

Pathan,

Sikander,

Aziz

et al. 2024

JISR-C

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The detection and diagnosis of brain tumors using conventional methods have enormous limitations and ambiguities. Purpose of this study is to identify Brain Tumor (BT) in CT scan by using emerging artificial intelligence paradigm i.e deep learning models. The primary objective is to leverage deep learning to advance the development of robust and reliable tools for early detection and diagnosis of brain tumors. Conventional methods for BT detection are no longer sufficient. and suitable approach for BT detection, as it is very sensitive and critical for human. So this study put an effort to evaluate the performance of deep learning models in recognizing BT in CT scans, with an additional focus on the development of a user-friendly dashboard using PHP for result visualization. The results of this research will contribute to the development of trustworthy tools that can aid medical professionals in the early detection and diagnosis of BT. To validate the effectiveness of the deep learning model, a comprehensive experimental evaluation is conducted using publicly accessible brain tumor datasets. The model's accuracy, sensitivity, specificity, and other relevant performance measures are rigorously assessed. Additionally, the study introduces a user-friendly dashboard developed in PHP to facilitate the intuitive display of results, enhancing the practicality of the deep learning model in a clinical setting. The experimental evaluation, using a substantial dataset of annotated BT images, confirms the effectiveness of the deep learning models in recognizing brain tumors in CT scans. The study provides valuable insights into the functionality, interpretability, and potential clinical application of the deep learning models for diagnosing brain tumors. This research contributes to ongoing efforts in BT treatment, while also aiming to improve patient care and outcomes.

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Section: Image Processing and Predictive Precisionmentioning

confidence: 85%

An Efficient GPU/Deep Learning Model Approach for Brain Tumor Detection in Pakistan

Pathan,

Sikander,

Aziz

et al. 2024

JISR-C

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“…However, determining the initial curve plays a vital role in the segmentation of desired region. [ 20 21 22 23 ] If the initial curve is far from the region which we want to extract, the segmentation result will not be proper. [ 24 25 26 ] Therefore, here, we have used the image histogram for initial curve detection.…”

Section: Introductionmentioning

confidence: 99%

Constructing Multiwavelet-based Shearlets and using Them for Automatic Segmentation of Noisy Brain Images Affected by COVID-19

Aghazadeh,

Moradi,

Noras

2023

Journal of Medical Signals &Amp; Sensors

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Backgorund: Nowadays, everybody's life is dominated by COVID-19, which might have been the source of severe acute respiratory syndrome coronavirus 2. This virus disrupts the lungs first of all. Recently, it has been found that coronavirus may affect the brain. Because all body actions rely on the brain, hence investigating its healthy is an essential item in coronavirus effects. Method: Brain image segmentation can be helpful in the detection of the regions damaged by the effects of coronavirus. Since every image given by photography devices may have noises, therefore, first of all, the brain magnetic resonance angiography (MRA) images must be denoised for best investigation. In the present paper, we have presented the construction of multishearlets based on multiwavelets for the first time and have used them for the purpose of denoising. Multiwavelets have some advantages to wavelets. Therefore, we have used them in the shearlet system to expand the properties of multiwavelets in all directions. After denoising, we have proposed a scheme for the automatic characterization of the initial curve in the active contour model for segmentation. Detecting the initial curve is a challenging task in active contour-based segmentation because detecting an initial curve far from the desired region can lead to unfavorable results. Results: The results show the performance of using multishearlets in detecting affected regions by COVID-19. Using multishearlets has led to the high value of peak signal-to-noise ratio and Structural similarity index measure in comparison with original shearlets. Original shearlets are constructed from wavelets whereas we have constructed multishearlets from multiwavelets. Conclusion: The results show that multishearlets can neutralize the effect of noise in MRA images in a good way rather than shearlets. Moreover, the proposed scheme for segmentation can lead to 0.99 accuracy.

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“…Region-based models aim to identify distinct regions by utilizing specific descriptors, such as color or intensity, to guide contour movement. Recently, these models have gained significant attention for their ability to capture regional data [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Initially designed to segment objects with uniform intensity, traditional region-based models, like the Chan-Vese (CV) model [8], excel at capturing images with similar intensity levels.…”

Section: Introductionmentioning

confidence: 99%

Selective image segmentation driven by region, edge and saliency functions

Soomro,

Niaz,

Soomro

et al. 2023

PLoS ONE

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Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.

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Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

Cited by 6 publications

References 33 publications

An Efficient GPU/Deep Learning Model Approach for Brain Tumor Detection in Pakistan

An Efficient GPU/Deep Learning Model Approach for Brain Tumor Detection in Pakistan

Constructing Multiwavelet-based Shearlets and using Them for Automatic Segmentation of Noisy Brain Images Affected by COVID-19

Selective image segmentation driven by region, edge and saliency functions

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