An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare
04 Jan 2022-Journal of Healthcare Engineering-Vol. 2022, pp 1-12
TL;DR: In this article , an ensembled spatial method for image enhancement was proposed, which employed the Laplacian filter, which highlights the areas of fast intensity variation, and then the gradient of the image was determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing.
Abstract: Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.
TL;DR: In this article , a morphological processing of residuals using a special kernel was proposed to improve the quality of medical images. But the major problems related to medical images are that most of the images (of different modalities) are suffered from noise and other quality-related problems such as poor contrast, blurring, and difficulties in extracting appropriate information.
Abstract: Medical imaging is playing a pivotal role in the domain of medical diagnosis. But the major problems related to medical images is that most of the images (of different modalities) are suffered from noise and other quality-related problems such as poor contrast, blurring, and difficulties in extracting appropriate information. Therefore, it is necessary to construct some techniques that could improve medical images in such a way that it will be ideal for diagnostic applications. Medical image enhancement is the process of increasing the contrast quality of intensity variations and improves the visual representation of medical images. To provide suitable interpretation and clearer image for the observers with reduced noise levels, this article proposes a novel competent medical image enhancement method based on morphologically processing of residuals using a special kernel. First, it combines linear low pass filtering with nonlinear technique that allows for selection of essential regions where edges will get well preserved. The selection of those regions is based on morphological processing of linear filter residuals and then aims to find significant regions specified by edges of high amplitude and appropriate size. The reconstructed regions are combined with the output of low pass filtering to recovers the original shape of edges. In addition, the method allows to control the contrast to avoid blurring while preserving significant image information. In the end, a special kernel is convolved with an image to obtain the sharper image. Experiments on the different types of medical images are performed and the results are compared using different standard evaluation metrics. The quantitative results obtained through proposed method for all the metrics are optimal compared to other competing methods. It is observed that the results on various test images affirm that the proposed method generates excellent image visual quality and significantly enhances the contrast of all images which helps with better diagnosis and treatment.
TL;DR: Zhang et al. as discussed by the authors proposed a multi-scale attention generative adversarial network (MAGAN) for medical image enhancement, which is trained in the confrontation between two generators and two discriminators.
Abstract: High quality medical images are not only an important basis for doctors to carry out clinical diagnosis and treatment, but also conducive to downstream tasks such as image analysis. Although many medical image enhancement methods have achieved good results, some of them still have shortcomings in homogenizing illumination distribution and maintaining texture details, and even introduce boundary artifact noise. In order to deal with these problems, this paper proposes a multi-scale attention generative adversarial network (MAGAN) for medical image enhancement, which is suitable for unpaired images. Our MAGAN is trained in the confrontation between two generators and two discriminators. It tries to fuse multi-scale information in feature extraction by establishing feature pyramid, and filters irrelevant activation to highlight important regions based on attention distribution, which is positive for imaging. Moreover, MAGAN strengthens the constraints on the quality of enhanced image from the perspectives of illumination distribution, texture details, deep semantic features and smoothness, so as to improve the enhancement effect. Compared with six state-of-the-art methods, the experimental results show that MAGAN has the most significant image enhancement effect, and also performs best in the downstream task of image segmentation.
TL;DR: In this paper , the authors retracts the article DOI: 10.1155/2022/9660820] and propose a new version of the article with a different title.
Abstract: [This retracts the article DOI: 10.1155/2022/9660820.].
TL;DR: In this article , a modified sun flower optimization (MSFO) method was used to solve the image enhancement problem in medical image processing, where the image quality was analyzed on six performance metrics and compared over several approaches.
Abstract: Image enhancement (IE) is a process which improves the contrast of image by sharpening the edge pixels intensity. This technique has attained much attention in medical field and several enhancement techniques are proposed by researchers. In image processing, the enhancement is regarded as complex optimization issues. This work introduces an efficient model to solve optimization issues using a modified optimization approach. Initially, the input medical images are denoised using Modified median filter (MMF) filter. Then these denoised images are enhanced for the further process. The enhancement is carried out by pixel intensity of image. The parameters like entropy, edge information and intensity are optimized by modified sun flower optimization (MSFO). This optimization is used for increasing the convergence speed. The overall evaluation is carried in Matlab platform. The image quality is analyzed on six performance metrics and compared over several approaches and provided better results. The experimentation is evaluated on five medical images and the Mean square error (MSE) and peak signal noise ratio (PSNR) achieved by the medical image 1 are 0.02 and 43.7 respectively.
TL;DR: In this article, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature; however, the proposed approach with very reliable and comparable performance will boost the fast and robust detection of coronavirus disease using chest X-ray images.
Abstract: Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
TL;DR: An approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images and the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique.
Abstract: The use of computer-aided diagnosis in the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal, non-COVID lung opacity and COVID-19 CXR images. To the best of our knowledge, this is the largest public COVID positive database. Ground glass opacity is the common symptom reported in COVID-19 pneumonia patients and so a mixture of 3616 COVID-19, 6012 non-COVID lung opacity, and 8851 normal chest X-ray images were used to create this dataset. Five different image enhancement techniques: histogram equalization, contrast limited adaptive histogram equalization, image complement, gamma correction, and Balance Contrast Enhancement Technique were used to improve COVID-19 detection accuracy. Six different Convolutional Neural Networks (CNNs) were investigated in this study. Gamma correction technique outperforms other enhancement techniques in detecting COVID-19 from standard and segmented lung CXR images. The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96.29%, 96.28%, 96.29%, 96.28% and 96.27% respectively. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11 %, 94.55 %, 94.56 %, 94.53 % and 95.59 % respectively for segmented lung images. The proposed approach with very high and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
TL;DR: A new ML-method is proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-CO VID-19 person, using new Fractional Multichannel Exponent Moments (FrMEMs).
Abstract: COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.
18 Apr 2008
TL;DR: Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover and is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is appropriate for researchers who wish to extend their knowledge in the area of color image processing.
Abstract: An introduction to color in three-dimensional image processing and the emerging area of multi-spectral image processing The importance of color information in digital image processing is greater than ever. However, the transition from scalar to vector-valued image functions has not yet been generally covered in most textbooks. Now, Digital Color Image Processing fills this pressing need with a detailed introduction to this important topic. In four comprehensive sections, this book covers: The fundamentals and requirements for color image processing from a vector-valued viewpoint Techniques for preprocessing color images Three-dimensional scene analysis using color information, as well as the emerging area of multi-spectral imaging Applications of color image processing, presented via the examination of two case studies In addition to introducing readers to important new technologies in the field, Digital Color Image Processing also contains novel topics such as: techniques for improving three-dimensional reconstruction, three-dimensional computer vision, and emerging areas of safety and security applications in luggage inspection and video surveillance of high-security facilities. Complete with full-color illustrations and two applications chapters, Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover. It is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is also appropriate for researchers who wish to extend their knowledge in the area of color image processing.
TL;DR: It is presented that two-dimensional both, the Haar and wavelets functions products man be treated as extractors of particular image features and shown that some coefficients from both spectra are proportional, which s slightly computations and analyses.
Abstract: Image processing and analysis based on the continuous or discrete image transforms are classic techniques. The image transforms are widely used in image filtering, data description, etc. Nowadays the wavelet theorems make up very popular methods of image processing, denoising and compression. Considering that the Haar functions are the simplest wavelets, these forms are used in many methods of discrete image transforms and processing. The image transform theory is a well known area characterized by a precise mathematical background, but in many cases some transforms have particular properties which are not still investigated. This paper for the first time presents graphic dependences between parts of Haar and wavelets spectra. It also presents a method of image analysis by means of the wavelets-Haar spectrum. Some properties of the Haar and wavelets spectrum were investigated. The extraction of image features immediately from spectral coefficients distribution were shown. In this paper it is presented that two-dimensional both, the Haar and wavelets functions products man be treated as extractors of particular image features. Furthermore, it is also shown that some coefficients from both spectra are proportional, which s slightly computations and analyses.
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