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.

In this paper, we propose a robust variational segmentation model capable of overcoming the problem of the negative effects of outliers. The proposed method is based on the combination of the characteristics of the generalized mean with the concept of the active contours. The optimization problem raising in this combination employs a power method technique. We demonstrate the performance of the proposed model on a series of sample images from diverse modalities and show an outperforming proposed model in comparison with the state-of-the-art methods. The proposed method shows better or equivalent performance in terms of accuracy and robustness than the conventional state-of-the-art models. The validation of the efficiency of the proposed two-phase algorithm is further extended to vector-valued images and multi-phase formulation.

Automated segmentation of fine objects details in a given image is becoming of crucial interest in different imaging fields. In this paper, we propose a new variational level-set model for both global and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the presence of noise. The proposed method maintains the same performance on clean and noisy vector-valued images. The model utilizes a combination of locally computed denoising constrained surface and a denoising fidelity term to ensure a fine segmentation of local and global features of a given image. A two-phase level-set formulation has been extended to a multi-phase formulation to successfully segment medical images of the human brain. Comparative experiments with state-of-the-art models show the advantages of the proposed method.

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.

Statistical characterization of morphological changes of dendritic spines is becoming of crucial interest in the field of neurobiology. Automatic detection and segmentation of dendritic spines promises significant reductions on the time spent by the scientists and reduces the subjectivity concerns. In this paper, we present two approaches for automated detection of dendritic spines in 2-photon laser scanning microscopy (2pLSM) images. The first method combines the idea of dot enhancement filters with information from the dendritic skeleton. The second method learns an SVM classifier by utilizing some pre-labeled SIFT feature descriptors and uses the classifier to detect dendritic spines in new images. For the segmentation of detected spines, we employ a watershed-variational segmentation algorithm. We evaluate the proposed approaches by comparing with manual segmentations of domain experts and the results of a noncommercial software, NeuronIQ. Our methods produce promising detection rate with high segmentation accuracy thus can serve as a useful tool for spine analysis.