Lavdie Rada

Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

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 L2 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 features boundaries (i.e., H1 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 features 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 observers annotations.

A restarted iterative homotopy analysis method for two nonlinear models from image processing

Total variation (TV) minimization-based nonlinear models have been proven to be very useful and successful in image processing. A lot of effort has been devoted to overcome the nonlinearity of the model and at the same time to obtain fast numerical schemes. In this paper, we propose a restarted iterative homotopy analysis method (HAM) to improve the computational efficiency for the TV models and will show by experiments that this method demonstrates great potential for recovering the noise and with great speed in both image denoising and image segmentation models. The method modifies the existing HAM and makes it suitable to potentially solve other nonlinear partial differential equations arising from image processing models. In our examples, we will demonstrate the validity of a restarted HAM and that this method is efficient and robust even for images with large ratios of noise and with much less CPU time than other methods.

Improved Selective Segmentation Model Using One Level-Set

Variational segmentation models have proven to be extremely efficient for segmenting and tracking boundaries and in most cases all of the boundaries in an image. Such models are for global segmentation. For a large class of image segmentation tasks where only one object is required to be extracted automatically, global models cannot deliver the solution and we need selective segmentation techniques. In this paper, we propose a novel, variational and single level-set function for the selective segmentation task. The model is much faster to implement than the previously dual level set model by Rada-Chen [29] by having the same efficiency and reliability. In comparing with interactive image segmentation algorithm of Nguyen-Cai-Zhang-Zheng method [2], our model shows some improvement in some cases. Several new ideas are incorporated in this new work: i) the distance function is only needed optionally and its inclusion does not affect the result; ii) an adaptive parameter is introduced in the edge detection function; iii) a new area-based fitting term is added to enhance the models reliability (different from the idea of minimizing the area of the object). We develop an additive operator splitting method for solving the resulting Euler-Lagrange equation. Test results show that the new model finds the desired local boundaries successfully in various challenging cases and indeed it is not much dependent on the prior information of markers or the distance function based on them. More importantly, the new model gives an overall improvement over the previous models and can be recommended for selective segmentation.

Automatic dendritic spine detection using multiscale dot enhancement filters and SIFT features

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.

Nonparametric joint shape and feature priors for segmentation of dendritic spines

Multimodal shape density estimation is a challenging task in many biomedical image segmentation problems. Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. Such density estimates are only expressed in terms of distances between shapes which may not be sufficient for ensuring accurate segmentation when the observed intensities provide very little information about the object boundaries. In such scenarios, employing additional shape-dependent discriminative features as priors and exploiting both shape and feature priors can aid to the segmentation process. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors using Parzen density estimator. The joint prior density estimate is expressed in terms of distances between shapes and distances between features. 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 dendritic spine segmentation in 2-photon microscopy images which involve a multimodal shape density.

Nonparametric Joint Shape and Feature Priors for Image Segmentation

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.

A variational model and its numerical solution for local, selective and automatic segmentation

Variational region-based segmentation models can serve as effective tools for identifying all features and their boundaries in an image. To adapt such models to identify a local feature defined by geometric constraints, re-initializing iterations towards the feature offers a solution in some simple cases but does not in general lead to a reliable solution. This paper presents a dual level set model that is capable of automatically capturing a local feature of some interested region in three dimensions. An additive operator spitting method is developed for accelerating the solution process. Numerical tests show that the proposed model is robust in locally segmenting complex image structures.

Tracking-assisted Detection of Dendritic Spines in Time Lapse Microscopic Images

Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.

Coupled shape priors for dynamic segmentation of dendritic spines

Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results.

An improved model for joint segmentation and registration based on linear curvature smoother

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope.