Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

The paper presents a novel semi-blind watermarking scheme for image copyright protection, which is developed in the Lifting Wavelet Transform (LWT) and is based on Singular Value Decomposition (SVD). We have been used fractal decoding to make a very compact representation of watermark image. In the embedding phase of watermarking scheme, at first; we perform decomposing of the host image with 2D-LWT transform, then SVD is applied to sub-bands of transformed image, and embed the watermark by modifying the singular values. In the watermark extraction phase, the embedded codes are extracted from the watermarked image. Then the watermark image is rendered by running the extracted code. The experiments indicate that the watermark is robust against the different attacks, such as average filter, Jpeg compression, rotation and etc.

Accurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. CE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. The approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. The MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Introduction and importance: Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm. Case presentation and methods: The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340). Clinical discussion: The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours. Conclusion: The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Digital watermarking has been proposed as a way to claim ownership. In this paper, a new approach in digital image watermarking based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is presented. We use compositional pattern producing networks (CPPNs) to make a very compact representation of watermark. Using Neuro Evolution of Augmenting Topologies (NEAT) will evolve the CPPN structure to produce a suitable watermark image. In the embedding phase, at first we perform decomposing of the host image with 2D-DWT transform at 5-level, then the SVD is applied to LH3-LH5 sub-bands of transformed image, and embed the watermark by modifying the singular values. In watermark extraction phase, the embedded coefficients of CPPN neat are extracted from the watermarked image. Then the watermark image is rendered by CPPN. The experiments indicate that the watermark is robust against the different attacks, such as average filter, Jpeg compression and etc.