Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is e...

This paper presents some aspects of the (gray level) image binarization methods used in artificial vision systems. It is introduced a new approach of gray level image binarization for artificial vision systems dedicated to the specific class of applications for moving scene in industrial automation – temporal thresholding. In the first part of the paper are remarked some limitations of using th...

BACKGROUND AND PURPOSE Texture analysis is an image processing technique that can be used to extract parameters able to describe meaningful features of an image or ROI. Texture analysis based on the gray level co-occurrence matrix gives a second-order statistical description of the image or ROI. In this work, the co-occurrence matrix texture approach was used to extract information from brain M...

A new generalization of the Gray map is introduced. The new generalization Φ : Z 2 → Z k−1 n 2 is connected with the known generalized Gray map φ in the following way: if we take two dual linear Z2k -codes and construct binary codes from them using the generalizations φ and Φ of the Gray map, then the weight enumerators of the binary codes obtained will satisfy the MacWilliams identity. The cla...

Microscopic images of leaves, collected from Mona Island dry forest (which is located between Puerto Rico and the Dominican Republic), were analyzed. For each leaf side an image was obtained at two magnifications (200x and 400x). This resulted in four samples of images showing a wide variety of textures and stomata patterns. For each group of images we used the gray-level co-occurrence method t...

In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the s...

Image segmentation is a key step of oil spills detection in SAR images. For the problem that the traditional multi-spectral clustering algorithm with the features extraction by GLCM (Gray-Level Co-occurrence Matrix) has such limitations as direction sensitivities and difficulties in selecting the best feature combination etc., this paper proposes a multi-scale segmentation method of oil spills ...

Fabric defect detection and classification plays an important role in inspection of fabric products. Many fabric defects are very small and undistinguishable, which can be detected only by monitoring the variation in the intensity. Currently, in almost all the fabric industries the process of defect detection is done manually using skilled labor. An automated defect detection and identification...

Computed tomography images are widely used in the diagnosis of brain tumor because of its faster processing, avoiding malfunctions and suitability with physician and radiologist. This study proposes a new approach to automated detection of brain tumor. This proposed work consists of various stages in their diagnosis processing such as preprocessing, anisotropic diffusion, feature extraction and...

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extra...