Color and Texture Feature Extraction Using Gabor Filter - Local Binary Patterns for Image Segmentation with Fuzzy C-Means

Image segmentation to be basic for image analysis and recognition process. Segmentation divides the image into several regions based on the unique homogeneous image pixel. Image segmentation classify homogeneous pixels basedon several features such as color, texture and others. Color contains a lot of information and human vision can see thousands of color combinations and intensity compared with grayscale or with black and white (binary). The method is easy to implement to segementation is clustering method such as the Fuzzy C-Means (FCM) algorithm. Features to beextracted image is color and texture, to use the color vector L* a* b* color space and to texture using Gabor filters. However, Gabor filters have poor performance when the image is segmented many micro texture, thus affecting the accuracy of image segmentation. As support in improving the accuracy of the extracted micro texture used method of Local Binary Patterns (LBP). Experimental use of color features compared with grayscales increased 16.54% accuracy rate for texture Gabor filters and 14.57% for filter LBP. While the LBP texture features can help improve the accuracy of image segmentation, although small at 2% on a grayscales and 0.05% on the color space L* a* b*

Hybrid Features of Mask Generated with Gabor Filter for Texture Analysis and Sobel Operator for Image Regions Segmentation Using K-Means Technique

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical model

DIAGNOSIS OF GLAUCOMA USING SUPERPIXEL CLASSIFICATION METHOD

Glaucoma is a global health problem expected to affect millions of people in world wide. Glaucoma is a chronic eye disease of the optic nerve and a leading cause of blindness and vision loss in worldwide. If glaucoma is not diagnosed and indulgence in time, it can steps forward to loss of vision and even blindness. Now a days several methods is used to detect and assessment of glaucoma such as intraocular pressure (IOP), abnormal visual field and assessment of damaged optic nerve head. The intraocular pressure measurement is performed using non-contact tonometry, but it is not sensitive for population based glaucoma screening. The assessment of abnormal visual field is performed by functional test through special equipment, but it is only present in territory hospitals and therefore unsuitable for screening. The Optic nerve head assessment can be done by a trained professional.   So to avoid these problems a new method is proposed for screening glaucoma using super pixel classification. The proposed system performs optic disc and optic cup segmentation. It uses the 2D fundus images. In optic disc segmentation, clustering algorithms are used to sort each superpixel as disc or non-disc, where as in optic cup segmentation the apart the clustering algorithms, gabor filter is also incorporated into the feature space to enhance the performance. The proposed method have been assessed based on the area of the optic disc and optic cup. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma for a given patient. A larger CDR indicates a higher risk of glaucoma. The proposed work is to be carried out using Matlab technical computing languag

Automated classification of retinopathy of prematurity in newborns

La Retinopatia de l'Prematur (ROP) és una malaltia que afecta els nadons prematurs mostrant-se com un subdesenvolupament dels vasos retinians. El diagnòstic precoç d'aquesta malaltia és un tot un repte ja que requereix de professionals altament qualificats amb coneixements molt específics. Actualment a Espanya, només uns pocs hospitals compten amb els equipaments especialitzats per al tractament i diagnòstic d'aquesta patologia. Aquest projecte final de màster, té com a objectiu final desenvolupar una eina preliminar per a la classificació de l'extensió aquesta malaltia. Aquesta applicació, ha estat disenyada per a ser integrada en una plataforma de suport a la diagnosi de la Retinopatia i poder evaluar la malaltia, proporcionant informació detallada sobre les imatge analitzades. Aquest projecte, també estableix les bases per a la comparació entre l'enfocament clínic, que utilitzen els metges, i la naturalesa "Black-Box" natural de la Xarxa Neuronal Artificial per classificar l'extensió de la malaltia. L'algoritme desenvolupat és capaç de: segmentar els vasos oculars utilitzant una xarxa neuronal convolucional U-Net; extreure les característiques representatives de la malaltia a partir de la segmentació; i classificar aquestes característiques en casos ROP i casos ROP Plus, mitjançant l'ús d'una gamma de classificadors. Les principals característiques analitzades són la tortuositat i el gruix dels vasos, indicadors de la malaltia emprats pels patolegs experts. La xarxa de segmentació ha obtingut una precisió global de l'96,15%. Els resultats dels diferents classificadors indiquen un trade-off entre la precisió i el volum d'imatges analitzades. S'ha obtingut una precisió de l'100% emprant un classificador de doble threshold en el analisis de l'12,5% de les imatges. En canvi, mitjançant l'ús d'un classificador "decision tree", s'ha obtingut una precisió del 70,8% analitzant el 100% de les imatges.La Retinopatía del Prematuro (ROP) es una enfermedad que afecta a los bebés prematuros mostrándose como el subdesarrollo de los vasos retinianos. El diagnóstico precoz de dicha enfermedad es un desafío ya que requiere de profesionales altamente capacitados con conocimientos muy específicos. Actualmente en España, solo unos pocos hospitales están dotados con los equipamientos especializados para el tratamiento y diagnóstico de esta patología Este proyecto final de master, tiene como objetivo final desarrollar una herramienta preliminar para la clasificación de la extensión dicha enfermedad. Esta aplicación, ha sido diseñada para ser integrada en una plataforma de soporte al diagnóstico de la Retinopatía y evaluar la enfermedad, proporcionando información detallada sobre las imágenes analizadas. Este proyecto también sienta las bases para la comparación entre el enfoque clínico, que utilizan los médicos, y la naturaleza "Black-Box" natural de la Red Neuronal Artificial para clasificar la extensión de la enfermedad. El algoritmo desarrollado es capaz de: segmentar los vasos oculares utilizando una red neuronal convolucional U-Net; extraer las características representativas de la enfermedad a partir de la segmentación; y clasificar estas características en casos ROP y casos ROP Plus, mediante el empleo de una gama de clasificadores. Las principales características analizadas son la tortuosidad y el grosor de los vasos, indicadores cauterizantes de la enfermedad empleados por los patólogos expertos. La red de segmentación ha logrado una precisión global del 96,15%. Los resultados de los diferentes clasificadores indican un trade-off entre la precisión y el volumen de imágenes analizadas. Se ha obtenido una precisión del 100% empleando un clasificador de doble threshold en el análisis del 12,5% de las imágenes. En cambio, mediante el uso de un clasificador “decision tree”, se ha obtenido una precisión del 70,8% analizando el 100% de las imágenes.Retinopathy of Prematurity (ROP) is a disease in preterm babies with underdevelopment in retinal vessels. Early diagnosis of the disease is challenging and requires skilled professionals with very specific knowledge. Currently, in Spain, only a few hospitals have departments specialized in this pathology and, therefore, are able to diagnose and treat it accordingly. This master project aims to develop the first preliminary instrument for the classification of the extent of Retinopathy disease. This tool has been built to be integrated into a diagnostic support platform to detect the presence of retinopathy and evaluate the sickness, providing insightful information regarding the specific image. This project also lays the base for the comparison between the clinical approach that the doctors use and the “black box” approach the Artificial Neural Network uses to predict the extent of the disease. The developed algorithm is able to: segment ocular vessels using a U-Net Convolutional Neural Network; extract the critical features from the segmentation; and classify those features into ROP cases and ROP Plus cases by employing a range of different classifiers. The main features analyzed by the related specialists and thus selected are tortuosity and thickness of the vessels. The segmentation Network achieved a global accuracy of 96.15%. The results of the different classifiers indicate a trade-off between accuracy and the volume of computed images. An accuracy of 100% was achieved with a Double Threshold classifier on 12.5% of the images. Instead, by using a Decision tree classifier, an accuracy of 70.8% was achieved when computing 100% of the images

Content Based Image Retrieval using CMM+GWT and SVM Classifier

Content based Image Retrieval Process Depending on New Matching Strategy. In this paper Proposed Model composed of four Major Phases: feature extraction, Dimensionality Reduction, ANN Classifier and Matching Strategy. feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern(DBPSP). Dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. Matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class