A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking

Mousavi, Seyed Mojtaba; Naghsh, Alireza; Abu-Bakar, S. A. R.

doi:10.1007/s10278-015-9770-z

Cited by 20 publications

(5 citation statements)

References 36 publications

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“…2 and Eq. 3 [18,19]. The mean PSNR and SSIM results for multimodal noise (in the range of 1% -98%) in each database are shown in Table 1.…”

Section: Proposed Methods and Resultsmentioning

confidence: 99%

An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

Ebrahimnejad

Naghsh

2022

Preprint

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Medical image noise reduction is a significant and challenging area in image processing. A new adaptive window-based solution for the removal of high-density multimodal salt-and-pepper noise of the brain MRI images is proposed in this paper. In this efficient method, for each pixel of the noisy input image, an adaptive n x n window is considered in the neighborhood of that pixel, where n depends on the noise value. The higher the noise density, the larger the window size in which healthy pixels are found. If they are not noisy, the pixels of this window are weighted according to their distance from the desired pixel. The greater the distance, the less weight they gain. Then, the weighted sum of the neighboring pixels is averaged, and the noisy pixel replaces with the resulting value. To evaluate the proposed method against multimodal salt-and-pepper noise, which simultaneously appears in an image from 1–98%, 208 images from seven MRI databases are applied. The results show the excellent performance of the proposed method. The mean Peak Signal to Noise Ratio (PSNR) of whole databases is 29.3465. As a preprocessing step, the efficient proposed method shows highly accurate results on the brain MRI images. After applying the noise removal method, the quality and the Structural Similarity (SSIM) increased. In this study, in addition to removing multimodal noise in an image, noise with a specific density (single mode) in each image is also removed with similar or better results.

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“…2 and Eq. 3 [18,19]. The mean PSNR and SSIM results for multimodal noise (in the range of 1% -98%) in each database are shown in Table 1.…”

Section: Proposed Methods and Resultsmentioning

confidence: 99%

An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

Ebrahimnejad

Naghsh

2022

Preprint

Self Cite

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“…Lorenz's system encrypts the sine map and ROI part which are required for the encryption. In [11] authors present a self-generating region of interest (ROI) method for watermarking application in biomedical images. This technique is robust enough to prevent many attacks such as Gaussian, median, sharpening, and wiener filters, which is the major advantage over other methods.…”

Section: Related Workmentioning

confidence: 99%

Histogram Based Visible Image Encryption for Real Time Applications

Kiran¹,

S²,

Bharath³

et al. 2022

J. Engg. Res. & Sci.

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Like most patient information, medical imaging data is subject to strict data protection and confidentiality requirements. This raises the issue of sending the data which contains a medical image on an open network as per the above issue, also there might be a leakage of information. Encrypting an Image and hiding the information in it is the Potential way of avoiding this problem. But there might be many problems when we try restoring the original image. As a solution to that, an algorithm dealing with region of intrest (ROI) in medical images based on the pixels of interest and histogram peak technique. Firstly Image histogram peak technique is used for calculating peaks in medical images. Then set the Threshold value to segregate the pixels of interest in the medical images. The threshold value can be calculated by taking an average of all peaks in the histogram. These pixels are encrypted with the help of the Sudoku matrix. The proposed scheme will be evaluated using a various test based on statistics along with those results which will be compared to benchmarks of the existing work. We can see the better performance in terms of security from the proposed technique.

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“…Another technique for extraction of ROIs from images is the use of threshold techniques. This approach has also gained research attention, as used in [50]- [55]. Most interestingly, the work by Ragab, et al [56] and Sheba and Gladston Raj [57] appear to be interesting in their performances and approaches.…”

Section: A Image Preprocessing: Segmentation and Croppingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking

Cited by 20 publications

References 36 publications

An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

Histogram Based Visible Image Encryption for Real Time Applications

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

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