Wood recognition is widely used in various areas and environments, e.g. construction industry, manufacturing, and ecology. The job is still being carried out mostly by wood identification experts until the present. Current implementations for wood recognition on computer systems are mainly desktop-based. Furthermore, they are not implemented using artificial intelligence techniques due to the v...

The adoptability of the heart to external and internal stimuli is reflected by rate variability (HRV). Reduced HRV can be a predictor post-infarction mortality. In this study, we propose an automated system predict diagnose congestive failure using short-term analysis. Based on nonlinear, nonstationary, highly complex dynamics failure, extracted multimodal features capture temporal, spectral, d...

Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is capable of evaluating fine structural changes in nuclear structure that otherwise are undetectable during standard microscopy analysis. In this artic...

Texture analysis is a well-known research topic in computer vision and image processing and has many applications. Gradient-based texture methods have become popular in classification problems. For the first time we extend a wellknown gradient-based method, Co-occurrence Histograms of Oriented Gradients (CoHOG) to extract texture features from 2D Magnetic Resonance Images (MRI). Unlike the orig...

The high-pace rise in the demands of medicinal plants towards pharmaceutical significances as well different ayurvedic or herbal remedials have forced agro-industries However, rising plant disease cases limited cumulative growth and hence both volumetric production quality medicine. In this paper a first its kind evolutionary computing driven ROI-specific hybrid deep-spatio temporal textural fe...

conclusions in this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. the obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy. results after classification with the ensemble supervised algorithm, the performa...

conclusions test-retest and correlation analyses have identified non-redundant radiomics features and this feature are prone to errors if they employed as quantitative biomarker for gbm image analysis. however when we use robust and redundant feature, quantitative image radiomics features are informative and prognostic biomarkers for gbm magnetic resonance imaging. results results shows that th...

Orthoimages are aerial images where feature displacements and scale variations have been removed. This type of images is widely used to calculate areas, determine land cover and land use, among others. This paper introduces a rotation-invariant classification model for three common orthoimage regions: city, sea and forest areas, using only texture information (without color information). Our cl...

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. It often has a similar visual aspect with the cirrhotic parenchyma on which it evolves and with the benign liver tumors. The golden standard for HCC diagnosis is the needle biopsy, but this is an invasive, dangerous method. We aim to develop computerized, noninvasive techniques for the automatic diagnosis of HCC, bas...

We present a novel methodology for the automated segmentation of Glioblastoma Multiforme tumors given only a highresolution T1 post-contrast enhanced channel, which is routinely done in clinical MR acquisitions. The main contribution of the paper is the integration of contextual filter responses, to obtain a better class separation of abnormal and normal brain tissues, into the multilevel segme...