Comparative Analysis of Color Edge Detection for Image Segmentation

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

Common carotid artery (CCA) ultrasound with estimation of Intima Media Thickness (IMT) is the safe and non-invasive technique for predicting the cardiovascular risks. The precise quantification of IMT is useful for evaluating the risk of cardiovascular disease. The presence of speckle noise in carotid ultrasound image reduces the quality of the image and automatic human interpretation. Carotid ultrasound images have multiplicative speckle noise and it is difficult remove compared to the additive noises. The speckle removal filters have a greater restriction in edges and characteristics preservation. In this paper, we propose an extension of our earlier work with a fully automated Region of Interest (ROI) extraction and speckle denoising using optimized bayesian least square estimation (BLSE) approach followed by edge detection. The objective of the paper is to reduce the speckle noise in the extracted ROI of carotid ultrasound images using state-of-art denoising techniques and then followed by edge detection techniques and compared them with the edges extracted by these edge operators of ground truth image. The proposed algorithm experiments with 50 B-mode carotid ultrasound images. Experimental analysis shows that proposed method achieves better results as compared to other edge detection methods in terms of structural similarity Index Map (SSIM), correlation of coefficient (CoC), peak signal to noise ratio (PSNR) and mean square error (MSE) measures. Based on results, proposed work more effective in terms of visual inspection and detail preservation in carotid ultrasound images.

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems.

There are numerous applications based on edge detection have been used in the area of image analysis. The technique of edge detection is an important step towards the visual system reliability and security that delivers a better understanding in many applications like object recognition classification, photography, and many more others computer vision application such as pedestrian detection for a vehicle on the road, face detection in biometric, and video surveillance. We know that detection of edge detection is a scientific technique that is practiced to provide better image analysis and towards this purpose, lots of edge identification approach was already implemented by the researchers in the image processing era, but they do not achieve acceptable results for all types of the image that can help in the image analysis. In this research, we introduced a comparative evaluation of edge detection algorithms for instance Sobel, Canny, and Fuzzy logic-based edge detector with an Improved Fuzzy Inference (IFI) system is presented to preserve image edge for image analysis. The key contribution of this research is developing a new hybrid edge mechanism by utilizing the gradient and standard deviation based fuzzy logic approach to achieve better edge detection efficiency. To provide a better edge or non-edge region from an image the proposed IFI has its impact on quality parameters, for instance, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Entropy and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of the proposed IFI system is compared with other edge technique and we observed that the achieved results justify the proposed work in image processing.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

Background:Common carotid artery (CCA) ultrasound with measurement of intima-media thickness (IMT) is a safe and noninvasive technique for assessing subclinical atherosclerosis and determining cardiovascular risks. Moreover, the pattern of wall thickening in the brachial artery (BA) is rather diffuse compared to the carotid artery and may be a more sensitive indicator of long-term systemic exposure to risk factors. Therefore noninvasive evaluation of mechanical parameters changes of both arteries has gained the attention of researchers.Objectives:The aim of this study was to compare different edge detection techniques with speckle reducing anisotropic diffusion (SRAD) de-noising filter in ultrasound images of both arteries.Patients and Methods:In a cross-sectional design, an examination was performed on ten men with mean age of 40 ± 5 years from September 2012 to March 2013 through random sampling. An ultrasonic examination of the left CCA and BA was performed. The program was designed in the MATLAB software to extract consecutive images in JPEG format from the AVI. Another program was designed in the MATLAB software to apply regions of interest (ROI) on the IMT of the posterior wall of common carotid and brachial arteries. Next, different edge detections and SRAD filter were applied to the ROI, separately. Finally, the program measured mean-squared error (MSE) and peak signal to noise ratio (PSNR).Results:The lowest values of MSE and highest values of PSNR were achieved by Canny edge detection with de-noising SRAD filter for IMT of left CCA and BA in 90 frames.Conclusions:Based on the result, by measuring the MSE and PSNR, this study showed Canny edge detection with SRAD filter is better than other edge detections in terms of speckle suppression and details preservation in CCA and BA ultrasound images.

Reversible image watermarking schemes are used to protect ownership and copyrights of digital images. This paper proposes a novel reversible image watermarking scheme based on adaptive image interpolation, segmentation and additive prediction error expansion (PEE). Proposed interpolation comprises of weighted average of neighboring pixels by allocating higher and lower weights to less and more distant neighboring pixel values respectively. The proposed adaptive image interpolation focuses on detection of edges thus minimizing artifacts imposed by interpolation. The idea of embedding varying amount of watermark bits in different image segments has been explored. Simple linear iterative clustering (SLIC) based image segmentation is performed to separate very sharp, sharp, smooth and very smooth regions in image. Higher number of watermark bits are embedded in sharp regions by using additive prediction error expansion embedding technique. Simulations of proposed and existing techniques were performed on different images and compared using embedding capacity (EC), peak signal to noise ratio (PSNR), computational efficiency, image quality, mean square error (MSE), normalized cross correlation (NCC) and structural similarity index (SSIM). The experimental results show that proposed scheme achieves better results in terms of EC, PSNR, computational efficiency, image quality, MSE, NCC and SSIM as compared to existing techniques.

An efficient method of removing noise from the image while preserving edges and other details is a great challenge for researcher. Image denoising refers to the task of recovering a good estimate of the true image from the degraded image without altering and changing useful structure in the image such as discontinuities and edges. Various algorithm has been developed in past for image denoising but still it has scope for improvement. In this paper, we introduced an intelligent iterative noise variance estimation system which denoised the noisy image. Proposed algorithm is based on wavelet transform that denoised the noisy image by adding weighted highpass filtering coefficients in wavelet domain that is the novelty of the proposed work. Thereafter denoised algorithm further enhanced by adaptive wiener filter in order to achieve the maximum PSNR. Experimental results show that the proposed algorithm improves the denoising performance measured in terms of performance parameter and gives better visual quality. Mean Square Error (MSE), Root Mean Square Error (RmSE) and Peak Signal to Noise Ratio (PSNR) used as a performance parameters which measure the quality of an image.

This paper analyzes the quality of the six most significant operators to detect the edges through the parameters of the Peak Signal to Noise Ratio (PSNR), signal to noise ratio (SNR), Mean Square Error (MSE) and Entropy over dental images. The analysis results are presented graphically and in tables. Model analysis was performed through the conversion of primary images in the pictures with different edge detection, and analysis of images obtained through the above parameters and varying degrees of bits per pixel (bpp). The analysis shows that operators Pyramid and Roberts give much better results at lower levels of bpp, whilst, according to Entropy of image, best results are being given by Pyramid operator. The study is consisted of two parts: first analyzing the parameters PSNR, SNR and MSE through bpp and afterwards analyzing Entropy images and level of detail.

Wavelet is one method that can be used as a step to recognition an individual . Wavelet is used to perform an image feature extraction . In the process of image texture analysis , showed the value of the coefficients of a wavelet . The magnitude of the coefficients obtained using wavelet feature extraction is influenced by several factors . Besides affected by the type of wavelet used , is also influenced by the magnitude of the wavelet decomposition level of itself. In the wavelet is known as the wavelet energy retention , which means the number of retained energy after undergoing a process of decomposition and cutting coefficients . During the decomposition process , the calculation for texture analysis is often a constraint . In order for the current calculation lighter texture analysis , necessary to the process of cutting coefficients based on the retention factor of the wavelet coefficients . Based on these issues , created a program to analyze the influence of variations in the level of wavelet decomposition and coefficient of variation coefficient of cutting a cut on the image . The object of this final project is 30 iris image that has been converted into polar form presented . Having experienced the process of cutting coefficients , to prevent the image of the reverse transformation has a big difference to the original image , in this study was calculated Mean Square Error ( MSE), Peak Signal to Noise Ratio ( PSNR), and Euclidean distance to determine the level of similarity of image input and output images . The Highest values of Retention ​, MSE and the Euclidean distance is obtained at the level of decomposition of 1 and the lowest at the level of decomposition 6 . While the truncated coefficient and PSNR values ​​obtained at the highest level of decomposition of 6 and the lowest level of decomposition of 6 . The variation coefficient of pieces , value retention and highest PSNR obtained at koefsien piece 5 and the lowest coefficient of 50 pieces . While the coefficient value is truncated , MSE and the highest Euclidean distance is obtained at koefsien pieces 50 and the lowest coefficient of 5 pieces . Keywords: iris, texture analysis, Haar wavelet transform, MSE, PSNR, E uclidean distance

Color Image Enhancement based on Gamma Encoding and Histogram Equalization

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

Image quality, which deteriorates primarily due to noise and distortions, is critical for the effectiveness of magnetic resonance imaging (MRI) analysis. This distortion is caused because of poor imaging settings or transmission system interference. MRI scans are typically corrupted by Rician noise when acquisition takes place. This noise should be eliminated from MRI as a pre-processing phase. This paper proposes MRI denoising using deep neural network (DNN). To extract image features from noised image, the model employs a collection of convolutions. The model too uses an encoder-decoder arrangement to preserve important image features whereas rejecting irrelevant features. To reduce noise in MRI, this paper proposed DNN based technique and this technique was compared with various denoising techniques such as median filter, gaussian filter, average filter, nonlocal means filter, bilateral filter, wavelet smoothed filters. These approaches were compared using measures like peak signal to noise ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE). The results demonstrates that the suggested model can efficiently denoise MRI scans while preserving essential image features.

Malaria is an extensively prevalent blood infection, the most severe and widespread parasitic disease that stirring millions of people in the world. Currently, microscopy diagnosis still the most widely used method for malaria diagnosis. However, this procedure contains the probability of miscalculation of parasites due to human error. Computerized system by using image processing is recognized as a quick and easy ways to analyze a lot of blood samples. However, because of the non-standard preparation of the blood slides which producing color varieties in different slides will result on low quality images. Hence, it is difficult to identify the existence of malaria parasites as well as observing its morphological characteristics to recognize malaria parasites. Therefore, this paper aims to analyze the standardization performance between six types of color constancy algorithms namely, gray world (GW), white patch (WP), modified white patch (MWP), progressive hybrid (PH), shades of gray (SoG) and gray edge (GE) on both thick and thin blood smear malaria images of P. falciparum and P. vivax species. Six types of color constancy algorithms standardization performance are analysed by using quantitative measure namely, peak signal to noise ratio (PSNR), normalized absolute error (NAE), mean square error (MSE) and root mean square error (RMSE). Based on the qualitative and quantitative findings, the results show that SoG algorithm is the best color constancy as compared to others proposed color constancy. SoG algorithm has achieved the highest PSNR and lowest NAE, MSE and RMSE values, thus proved that the quality of malaria images have been improved.