Zernike Moments and Genetic Algorithm: Tutorial and Application

In this paper, we propose an approach in microscopic image classification for histological sections of human tissues. The method is based on image descriptors composed of vectors of accumulated Zernike moments. The goal is to construct a robust and precise method of image recognition and classification that can be applied in the case of histological tissue samples. Thanks to their properties Zernike moments fit these requirements. Additionally, processed Zernike moments can be made scale, translation, and rotation invariant. In a series of experiments, we verify the effectiveness of the method and its application to the presented problem of medical image classification. The results are obtained with the help of predefined classifiers provided by dedicated software. The paper presents a comparison of results and proposes an example method of improving the approach.

<p>Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including: three color space components and two Zernike moments with different order are gathered and utilized as an input of fuzzy inference system to materialize a robust rotation/lighting condition and size invariant image classifier. For better performance, all the membership functions are optimized by genetic algorithm after empirically design stage. 93.07% and 95.25% classification rates empirically design and optimized systems confirm the reliability of proposed method in different image conditions given in this contribution.</p>

We consider the use of Zernike moments (ZMs) for rotation- and scale-invariant classification of images. It is well known that ZMs are rotation-invariant only. We make use of the major benefit of the Fourier-Mellin (FM) transformation, which changes the rotation and the scale into translation. We introduce a new algorithm, which fuses the ZMs with the FM transform and is invariant under both rotation and scaling. Two sets of images were used to test the proposed algorithm. Experimental results reveal that the proposed algorithm has much better recognition rate than using ZMs within a variation of rotation between 0 and 360 deg, and scaling down and up between 25% and 400% of the original size.

In order to solve the problem that robustness in copying and pasting forgery detection algorithm is poor and detection accuracy is insufficient, this paper puts forward to image forgery detection algorithm of coupling Zernike Moment (Zernike) by Undecimated Wavelet Transform (UWT).Firstly, it uses Undecimated Wavelet Transform to break the input image up into approximation coefficient LL and specific coefficient HH and get similarity and difference among blocks; then it divides overlapping block and calculate space between image subblock, makes order and classification according to similarity LL and difference HH and introduces Zernike Moment, builds distance matrix for image feature matching and image forgery detection.The experiment indicates that the mentioned algorithm has good translation invariance and amplitude rotation invariance and it has good anti-post processing performance, high accurate measurement, low omission factor and false positive rate.

The authors presented a novel system for automated nodule detection in lung CT exams. The approach is based on (1) a lung tissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments. The system was validated on 154 chest axial CT exams provided by the lung image database consortium public database. The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 6.5 false positives per patient (FP/patient). A higher specificity of 2.5 FP/patient was reached with a sensitivity of 60%. An independent test on the ANODE09 competition database obtained an overall score of 0.310. The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

Image retrieval and classication using shifted Legendre invariant moments and Radial Basis Functions Neural Networks

with the remarkable growth in image processing, the requirements for dealing out with blurred images is difficulty in a variety of image processing applications. In this paper presents the restoration of blurred images which gets degraded due to diverse atmospheric and environmental conditions, Blur is a key determinant in the sensitivity of image quality, so it is essential to restore the original image. The research outcomes exhibit the major identified bottleneck for restoration is to deal with the blurred images and also a set of attempts have been executed in image restoration using multiple moment algorithms. However the precise results are not been proposed and demonstrated in the comparable researches. Also detail understanding for applications of moment algorithms for image restoration and demonstrating most suitable moment method is current requirements for research. Hence in this work we employ most accepted moment algorithms to exhibit the effect of moments for image restoration and the performance of the moment algorithms such as the Hu, Zernike and Legendre moments is evaluated on image with different blurring lengths. Moreover the effect of moment algorithms is also demonstrated in order to find the optimal setting of orders for image restoration. The final outcome of this work is a stable version of MATLAB based application to visually demonstrate the performance difference of Hu, Zernike and Legendre moments. The relative performance of the application is also been demonstrated with the help of multiple image datasets of biometric identifier such as fingerprint, hand palm and human face.

A novel hybrid technique of combining an improved morphological gradient operator and Zernike moments, named MGZ edge detection algorithm, is developed to meet the stringent requirements for measuring accuracies of charge coupled device metrology system. The mathematical morphological gradient and Zernike moment algorithm are discussed based on the pixels grey-scale and spatial structure information of the images. To improve the algorithm process, the image edge is oriented and extracted by using improved morphological gradient operator firstly; then, the calculation errors of the typical ideal step edge model are analysed and corrected by the proposed error compensation formula. The edge point is relocated with sub-pixel accuracy by means of Zernike moment operator based on the edge point vectors and the threshold value. Finally, the sub-pixel edge detecting of the image is attained. The extensive experimental results show that the measuring error of the edge point is compensated reliably and accurately, and the new algorithm has short operation time, more precise and stronger robustness to noise than the prior similar algorithms. Meanwhile, the proposed method can well meet the need of charge coupled device metrology system for sub-pixel edge detection.

Brain tumor diagnosis based on Zernike moments and support vector machine optimized by chaotic arithmetic optimization algorithm

Various types of orthogonal moments have been widely used for object recognition and classification. This paper presents an effective way of extracting texture features, Bessel Fourier moments, for image retrieval and classification applications. The Bessel Fourier moments are calculated for rotation invariance and perform better in terms of represent global features than orthogonal Fourier-Mellin and Zernike moments. In order to achieve good results in CBIR experiments and image classification experiments using Bessel Fourier moments, we conduct experiments on four databases: the first one is a small 2D texture database formed by 400 images, and the rest are 2D color image databases with different relevant images formed by 1200 from the Amsterdam Library of Object Image (ALOI). The experiments show that the feature descriptors extracting with the proposed algorithm perform better for image retrieval and classification than conventional descriptors by comparing the retrieval accuracy, the same order.

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

Multispectral images contain richer spectral signatures than easily available RGB images, for which they are promising to contribute to information perception. However, the relatively high cost of multispectral sensors and lower spatial resolution limit the widespread application of multispectral data, and existing reconstruction algorithms suffer from a lack of diverse training datasets and insufficient reconstruction accuracy. In response to these issues, this paper proposes a novel and robust multispectral reconstruction network from low-cost natural color RGB images based on free available satellite images with various land cover types. First, to supplement paired natural color RGB and multispectral images, the Houston hyperspectral dataset was used to train a convolutional neural network Model-TN for generating natural color RGB images from true color images combining CIE standard colorimetric system theory. Then, the EuroSAT multispectral satellite images for eight land cover types were selected to produce natural RGB using Model-TN as training image pairs, which were input into a residual network integrating channel attention mechanisms to train the multispectral images reconstruction model, Model-NM. Finally, the feasibility of the reconstructed multispectral images is verified through image classification and target detection. There is a small mean relative absolute error value of 0.0081 for generating natural color RGB images, which is 0.0397 for reconstructing multispectral images. Compared to RGB images, the accuracies of classification and detection using reconstructed multispectral images have improved by 16.67% and 3.09%, respectively. This study further reveals the potential of multispectral image reconstruction from natural color RGB images and its effectiveness in target detection, which promotes low-cost visual perception of intelligent unmanned systems.

The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle swarm optimization (PSO) heuristics to optimize the k-means algorithm and examine the reliability of parametric values for different variants of PSO and k-means algorithms. These PSO heuristics include linear inertia reduction, constriction factor, and dynamic inertia and maximum velocity reduction. The performance of these hybridization of PSO and the k-means algorithms was tested on the image segmentation. These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results.

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.

This paper presents a new feature extraction method of Fourier descriptor based on the Zernike moments for pollen images recognition. Firstly, Zernike moments of the image are extracted in 3D spherical coordinates. Secondly, genetic algorithm based on probability is used to filter the Zernike moments to reduce redundant information. Finally the normalized Fourier transform coefficients are calculated as the last feature descriptor. The simulation results on Confocal dataset show that the descriptor can effectively describe the pollen images and is robust to the rotation, translation and scaling of the image.