Astronomical image processing based on fractional calculus: the AstroFracTool

The implementation of fractional differential calculations can give new possibilities for image processing tools, in particular for those that are devoted to astronomical images analysis. As discussed in arxiv:0910.2381, the fractional differentiation is able to enhance the quality of images, with interesting effects in edge detection and image restoration. Here, we propose the AstroFracTool, developed to provide a simple yet powerful enhancement tool-set for astronomical images. This tool works evaluating the fractional gradient of an image map. It can help produce an output image useful for further research and scientific purposes, such as the detection of faint objects and galaxy structures, or, in the case of planetary studies, the enhancement of surface details.Comment: Keywords: Fractional calculation, image processing, astronom

Liver CT enhancement using Fractional Differentiation and Integration

In this paper, a digital image filter is proposed to enhance the Liver CT image for improving the classification of tumors area in an infected Liver. The enhancement process is based on improving the main features within the image by utilizing the Fractional Differential and Integral in the wavelet sub-bands of an image. After enhancement, different features were extracted such as GLCM, GRLM, and LBP, among others. Then, the areas/cells are classified into tumor or non-tumor, using different models of classifiers to compare our proposed model with the original image and various established filters. Each image is divided into 15x15 non-overlapping blocks, to extract the desired features. The SVM, Random Forest, J48 and Simple Cart were trained on a supplied dataset, different from the test dataset. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of enhancement in the proposed technique

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation

Liver tumor detection by classification through FD enhancement of CT image

In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique

A comment on Eta Carinae's Homunculus Nebula imaging

Homunculus Nebula is surrounding the star system Eta Carinae. The nebula is embedded within a much larger ionized hydrogen region, which is the Carina Nebula. Homunculus is believed to have been ejected in a huge outburst from Eta Carinae in 1841, so brightly to be visible from Earth. This massive explosion produced two polar lobes and an equatorial disc, moving outwards. Though Eta Carinae is quite away, approximately 7,500 light-years, it is possible to distinguish in the nebula, many structures with the size of about the diameter of our solar system. Knots, dust lanes and radial streaks appear quite clearly in many images. In this paper, we compare the imaging of Homunculus Nebula has obtained by HST and Gemini South Telescope research teams. We use some processing methods, to enhance some features of the structure, such as the color gradient, and knots and filaments in the central part of the nebula.Comment: Astronomy, Image Processing, Edge detectio

Fractional order color image processing

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative

Palm Print Edge Extraction Using Fractional Differential Algorithm

Algorithm based on fractional difference was used for the edge extraction of thenar palm print image. Based on fractional order difference function which was deduced from classical fractional differential G-L definition, three filter templates were constructed to extract thenar palm print edge. The experiment results showed that this algorithm can reduce noise and detect rich edge details and has higher SNR than traditional methods

EL USO DEL PROCESAMIENTO DE IMÁGENES LOGARÍTMICAS APLICADO AL ANÁLISIS DEL MAPA DE ANOMALÍA GRAVITATORIA DE BOUGUER (ÁREA DE TANGER–TETUAN, MARRUECOS)

Image processing is a powerful tool for the enhancement of edges in images used in the interpretation of geophysical potential field data. Arial and terrestrial gravimetric surveys were carried out in the region of Tangier-Tetuan. From the observed and measured data of gravity Bouguer gravity anomalies map was prepared. This paper reports the results and interpretations of the transformed maps of Bouguer gravity anomaly of the Tangier-Tetuan area using the logarithmic image processing. Filtering analysis based on classical image process was applied. Operator image process like the logarithmic operator and the associated gamma correction tool are used. This paper also present the results obtained from this image processing analysis of the enhancement edges of the Bouguer gravity anomaly map of the Tangier-Tetuan zone.El procesamiento de imágenes es una herramienta poderosa para mejorar los bordes en imágenes utilizadas en la interpretación del potencial geológico de campos. Mediciones gravimétricas aéreas y terrestres se realizaron en la región de Tánger-Tetuán. A partir de los datos observados y medidos de la anomalía gravitatoria de Bouguer, un mapa fue creado. Este trabajo reporta los resultados y las interpretaciones de los mapas transformados de anomalía gravitatoria de Bouguer en la zona de Tánger- Tetuán utilizando el procesamiento de imágenes logarítmica. Se aplicó el análisis de filtrado basado en procesamiento de la imagen clásica. Así mismo, se utilizan operadores del procesamiento de imágenes como el operador logarítmico y la herramienta de corrección de gamma. Este artículo también presenta los resultados obtenidos del análisis del procesamiento de imágenes para mejor la detección de bordes en el mapa de anomalía gravitatoria de Bouguer de la zona de Tánger - Tetuán