Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.

In this paper, we design a novel variational segmentation method for two types of segmentation problems, namely, global segmentation (all objects /features in a given image are aimed to be segmented) and selective/ interactive segmentation (an objects /feature of interest in a given image is aimed to be segmented) for inhomogeneous and severe additive noisy images. The proposed segmentation models implement a local denoising constraint, capable to tackle efficiently noise/outliers and coping with intensity inhomogeneity issues, combined with local similarity factor based on spatial distances and intensity differences in the local region that guides accurately the level set function to distinguish between outliers and minute important details. Furthermore, to exhibit the accuracy of the proposed models, an experimental comparison is inducted and shown comparisons with state-of-art models on synthetic images, outdoor images, and medical images.

Healthcare systems worldwide are burdened by mosquitoes transmitting dangerous diseases. Conventional mosquito surveillance methods to alleviate these diseases are based on expert entomologists’ manual examination of the morphological characteristics, which is time-consuming and unscalable. The lack of professional experts brings a high necessity for cheap and accurate automated alternatives for mosquito classification. This paper proposes an end-to-end deep Convolutional Neural Network (CNN) for mosquito species classification by taking advantage of both dropout layers and transfer learning to enhance performance accuracy. Dropout layers randomly disable the neurons of the neural network, mitigating co-adaptation and data overfitting. Transfer learning efficiently applies the extracted features from one dataset to others. Furthermore, a Region of Interest (ROI) visualization component is adopted to gain insight into the model learning. The generalization ability and feasibility of the proposed model are validated on four publicly available mosquito datasets. Experimental results on these datasets with an accuracy of 98.82%, 98.92%, 94.66%, and 98.40% demonstrate the superiority of our proposed system over the recent state-of-the-art approaches. The effectiveness of different numbers of dropout layers, their positions in the network, and their values are all investigated through ablation studies. Visualizing the model attention confirms that useful mosquito features are learned from insect legs and thorax through our model leading to optimistic predictions.

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de‐hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post‐processing towards human assistance systems benefit. In this study, the authors present a variational segmentation model equipped with de‐hazing constraint terms in a new coupled dehazing‐segmentation model. The proposed hybrid formulation not only recovers/restores the fog/haze degradation but at the same time segments image degraded object/objects by solving in this way the difficulties of simultaneously performed dehazing and segmentation pre/post‐processing. This combination takes into account the image structure boundaries and the image quality, leading in this way to a robust dehazing segmentation scheme. The advantages of the proposed method are the suitability of the model for grey and vector‐valued images, a small number of parameters involved, and a rather good speed of the algorithm. Experiments show that their approach outperforms the state‐of‐the‐art algorithms in terms of segmentation accuracy while avoiding a dehazing preprocessing which reflects an extended CPU time.