Learning Non-Homogenous Textures and the Unlearning Problem with Application to Drusen Detection in Retinal Images

In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non- homogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself is a challenging problem due to high variability of non-homogenous texture appearance. We present preliminary results

Texture Image Segmentation using Morphology in Wavelet Transforms

One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segme- ntation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu2019;s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Revista ew on Brain Tumour Detection using Image Processing

The Automatic Support Intelligent System is used to detect Brain Tumor through the combination of neural network and fuzzy logic system. It helps in the diagnostic and aid in the treatment of the brain tumor. The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents an analytical method that enhances the detection of brain tumor cells in its early stages and to analyze anatomical structures by training and classification of the samples in neural network system and tumor cell segmentation of the sample using fuzzy clustering algorithm. The artificial neural network will be used to train and classify the stage of Brain Tumor that would be benign, malignant or normal. The Fast discrete curvelet transformation is used to analysis texture of an image. In brain structure analysis, the tissues which are WM and GM are extracted. Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. Decision making is performed in two stages: feature extraction using GLCM and the classification using PNN-RBF network. The segmentation is performed by fuzzy logic system and its result would be used as a base for early detection of Brain Tumor which would improves the chances of survival for the patient. The performance of this automated intelligent system evaluates in terms of training performance and classification accuracies to provide the precise and accurate results. The simulated result enhances and shows that classifier and segmentation algorithm provides better accuracy than previous methodologies