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This paper focuses on extracting and analyzing different spectral features from transrectal ultrasound (TRUS) images for prostate cancer recognition. First, the information about the images' frequency domain features and spatial domain features are combined using a Gabor filter and then integrated with the expert radiologist's information to identify the highly suspicious regions of interest (ROIs). The next stage of the proposed algorithm is to scan each identified region in order to generate the corresponding 1-D signal that represents each region. For each ROI, possible spectral feature sets are constructed using different new geometrical features extracted from the power spectrum density (PSD) of each region's signal. Next, a classifier-based algorithm for feature selection using particle swarm optimization (PSO) is adopted and used to select the optimal feature subset from the constructed feature sets. A new spectral feature set for the TRUS images using estimation of signal parameters via rotational invariance technique (ESPRIT) is also constructed, and its ability to represent tissue texture is compared to the PSD-based spectral feature sets using the support vector machines (SVMs) classifier. The accuracy obtained ranges from 72.2% to 94.4%, with the best accuracy achieved by the ESPRIT feature set.

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This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yielding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN.

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M YCOSES and plant fungal pathogens are limiting factors highly affecting public health and crop production. Some fungal strains have been documented to be resistant to the commonly used drugs. Therefore, finding out new and pivotal antifungal drugs is becoming a global priority. Herein, we evaluated the in vitro antifungal activities of different crude polar (methanol and ethyl acetate) and non-polar (chloroform and petroleum ether) extracts of the mostly untapped brown seaweed Hormophysa cuneiformis (order Fucales, Phaeophyceae), in the Egyptian coastal waters, against eight pathogenic fungi: Aspergillus flavus, A. fumigatus, Candida albicans and Trichosporonas ahii (as human pathogens), and Alternaria alternata, Cladosporium herbarum, Fusarium oxysporum and Penicillium digitatum (as plant pathogens). The agar well diffusion assay was applied. Our findings showed that the chloroform extract only exhibited a potential antifungal activity against all tested fungal isolates, particularly T. asahii, C. albicans, A. fumigatus and C. herbarum, while the other extracts had relatively no remarkable effects. The minimum inhibitory concentrations (MICs) ranged between 0.78 and 6.25µg.ml-1 and these values are very close to those of the standard antifungal drug amphotericin B (0.63-5µg.ml-1). GC-MS analysis of the crude chloroform extract revealed 45 different bioactive compounds, mainly including 18 different species of saturated, monounsaturated and polyunsaturated fatty acids (71.48%) and some essential oils. The major constituents were arachidonic (C20:4

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