nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Active Model Initialization via Poisson Inverse Gradient

Active models have been widely used in image processing applications. A crucial stage that affects the ultimate active model performance is initialization. This paper proposes a novel automatic initialization approach for parametric active models in both 2-D and 3-D. The PIG initialization method exploits a novel technique that essentially estimates the external energy field from the external force field and determines the most likely initial segmentation. Examples and comparisons with two state-of-the-art automatic initialization methods are presented to illustrate the advantages of this innovation, including the ability to choose the number of active models deployed, rapid convergence, accommodation of broken edges, superior noise robustness, and segmentation accuracy.
Morphological pyramid image registration

We propose an intensity-based morphological pyramid image registration algorithm. This approach utilizes the global affine transformation model, also considering radiometric changes between images. With the morphological pyramid structure, Levenberg-Marquardt optimization, and bilinear interpolation, this algorithm can be implemented hierarchically and iteratively with capability of measuring, to subpixel accuracy, the displacement between images subjected to simultaneous translation, rotation, scaling, and shearing. The morphological pyramid shows better performance than the Gaussian pyramid in this matching technique.
Active Contour External Force Using Vector Field Convolution for Image Segmentation

Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.
Slide: Saliency guided image dictionary and image similarity evaluation

In this paper we present a novel idea of evaluating similarity between two images aided by a salient object detection framework. For computing similarity between images consisting of multiple objects and varying background, extracting features relevant to the object of interest is of cruicial importance. To accomplish this task, we employ a saliency guided dictionary learning framework for image similarity evaluation (SLIDE). The saliency detection framework emphasizes the image regions that attract human attention and is exploited to build a dictionary for generating sparse representation of the images. The compressibility of the sparse codes is exploited in computing the similarity measure. The SLIDE framework is used in the application of image retrieval for three military image datasets and shows in average an improvement of 14.8% in retrieval accuracy compared to two state-of-the-art similarity measures - information distance and sparse representation based compression distance.
Ultrasound Despeckling Using an Adaptive Window Stochastic Approach

A novel stochastically driven filtering method to despeckle B mode ultrasound images is presented. This method is motivated by viewing the pixel values as a stochastic process and removing outliers, where outliers are defined by local extrema. These outliers are removed by local averaging. This produces another image with new outliers (local extrema) and the process is iterated. With each iteration homogeneous regions become smoother while edges that defined these regions are preserved. By allowing a dynamically varying window to determine the local mean, we achieve equivalent results with fewer iterations.
SDL: Saliency-Based Dictionary Learning Framework for Image Similarity

In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.
Adaptive image processing via snake filters

We propose an image enhancement and scaling technique using adaptive snakes that modify the image according to region area and perimeter. Snake filters consist of geometric active contours that eliminate image regions below the area scale and above the specified perimeter. The snake filters are efficiently implemented using front propagation via a fast marching method. We compare the snake filters to similar filters based on connected component analysis and area morphology. The paper demonstrates that snake filters improve upon the computational cost and the flexibility of area morphological filters such as the area open-close filter. Applications of object detection based on area and perimeter scales are given for biomedical images.
Feature extraction and image retrieval on an automata structure

An automata processor can execute pattern matching in parallel which brings potential to accelerate image recognition. In this paper, we present a novel process of implementing image retrieval using multinary representation for use on an automata framework. Images are encoded into discriminative and unique regular expression descriptors in such a way that can be used for classification purposes. The regular expression descriptors are streamed through sets of non-deterministic finite automata (NFA). Results show that image retrieval can be implemented on automata structures that can achieve dramatic improvement over general purpose processors or graphics processors in efficiency.
The morphological lomo filter for multiscale image processing

Locally monotonic (lomo) images are defined as root signals of a morphological lomo filter. The morphological approach allows a multidimensional generalization of local monotonicity. This generalization is well motivated in that it retains the essential properties of one-dimensional (1D) local monotonicity. Repeated application of the lomo filter produces a lomo root signal of a specified scale. By filtering at multiple scales, a locally monotonic scale-space can be created and used in multiscale image applications such as segmentation, tracking, content based retrieval, and image coding. In contrast to existing linear and nonlinear scale-generating filters, the lomo filter has no spatial or graylevel bias and preserves edge localization through scale-space.
LaWeCo: Active region detection in non-uniformly sampled data using Laplacian-weighted covariance

Event detection in sparse, non-uniformly sampled multidimensional data eludes traditional signal processing techniques. We propose a method that, within the graph-theoretic framework, successfully addresses the task of localizing and characterizing active regions by leveraging the coherent behavior detection capabilities of the covariance matrix together with the natural definition of graph neighborhood connectivity provided by the Laplacian. We further extend this framework using scale space formulation to detect the extent of active region. Our method exhibits significant improvement in detecting active regions compared to other similar state-of-the art techniques. The method is demonstrated on interferometric synthetic aperture radar (InSAR) data that is both sparse and non-uniformly sampled. Comparison with three widely used event detection methods reveals the efficacy and efficiency of the Laplacian-weighted covariance technique.
Speckle Reducing Anisotropic Diffusion for Echocardiography

Echocardiographic segmentation accuracy significantly affects the calculation of important cardiac parameters, which assist in the understanding, diagnosis, and treatment of cardiovascular disease. This paper investigates extensions to speckle reducing anisotropic diffusion (SRAD) for the purpose of improving automated segmentation in echocardiographic imagery. We first investigate windowed SRAD (WSRAD), which attempts to increase edge contrast while preserving object size via the addition of neighborhood statistics to the SRAD diffusion coefficient. We also consider deconvolutional SRAD (DeSpeRADo), which attempts to alleviate blurring artifacts inherent to the ultrasound imaging system. We quantify the segmentation effects of each filtering method via simulated cardiac studies. With respect to unfiltered segmentation results, we measured a 21%, 24%, and 22% increase in myocardial segmentation accuracy with SRAD, WSRAD, and DeSpeRADo, respectively.
A pyramidal algorithm for area morphology

A fast algorithm that approximates area morphological filters is presented. Area morphological filters enable the elimination of connected components within image level sets according to area alone. To date, area morphological filters have been limited in application due to the high computational expense involved with connected component analysis. The pyramidal implementation described here dramatically reduces the computational cost by simultaneously creating a marker image and performing reconstruction in a coarse-to-fine manner. The pyramidal algorithm reduces the computational cost by three orders of magnitude for standard-sized grayscale images. Image examples and an analysis of the computational expense are provided in the paper.
Freehand 3D Ultrasound Volume Reconstruction via Sub-Pixel Phase Correlation

3D ultrasound provides clinicians and researchers with more intuitive anatomical visualization as well as accurate volumetric measurements. A novel freehand transducer constructed by Hossack et al. allows for 3D reconstruction of cross-sectional slices through the use of additional tracking arrays. These tracking arrays produce a coplanar image sequence, perpendicular to the cross-sectional slices, from which frame-to-frame motion can be determined. Accurate and precise motion estimation is required for successful volume reconstruction. In this paper we propose a novel volume reconstruction technique for the freehand 3D ultrasound transducer utilizing sub-pixel phase correlation. Using a synthetic data set with known volumetric characteristics, we compare our algorithm to standard correlation techniques.
Multi-cell 3D tracking with adaptive acceptance gates

Recent advances in multiphoton microscopy have allowed the capture of dendritic and T cells on video in a 3D volume. This paper reports on an approach for automatically detecting and tracking the cells and collecting statistics on their characteristics of motion and interaction durations. A novel method to extend the track longevity is presented, where an adaptive acceptance gate is computed based on the local target density. Results are provided that show that the total number of track segments was reduced by 22% and the percentage of tracks that lasted longer than half the video increased from 12% to 17% of the total tracks.
Symmetry constrained shape evolution in shape manifolds for shape based retrieval

Shape manifolds provide mathematically consistent and rigorous characterization of shapes and their variability. Continuous boundary based representations offer advantages of unambiguous reproduction of shape compared to landmark based methods and obviates the need for manual selection of landmark points. An important drawback of continuous shape representations is their inability to constrain shape to meaningful deformation modes, thus making template based shape retrievals impossible. We build a class of symmetric generators that yield a systematic methodology to constrain shape deformations within a submanifold of the infinite dimensional shape manifold. Geodesic distances on this submanifold estimate dissimilarities in physically meaningful deformation modes. Induced Riemannian metrics on the submanifold can be used to calculate directional derivatives, which can in turn be used to perform analysis of energy functions in the shape space. We finally describe a real valued match metric on the Riemannian shape manifold which is minimized to obtain projections of arbitrary shapes into the constrained submanifold and can be applied to shape based object retrieval.
Incorporating variance within binary flow for leukocyte tracking

Tracking leukocytes in vivo is vital in determination of the roles and significance of integrins and selectins within the inflammation process and the effectiveness of anti-inflammatory drugs. In this paper, we propose a region-based snake for segmenting objects in the presence of inhomogeneities. We then use this geometric snake to track leukocytes in intravital microscopy. The results show that the binary flow algorithm introduced here improves upon the traditional binary flow algorithm and the gradient vector flow algorithm both in terms of percentage of frames tracked and the root mean squared error.
A decentralized image retrieval system for education

We describe the development of a decentralized image retrieval system for education (DIRECT). With DIRECT, content based image retrieval (CBIR) is a service that will run autonomously; the constituent collections will not need to be modified to accommodate DIRECT. DIRECT applies a consistent CBIR mechanism to the entire system and gives CBIR functionality to collections without effort by the collection providers. Collections that do not have their own CBIR system or are not able to invest in a CBIR system (such as small collections or collections that do not contain a large proportion of images) will gain greater distribution of their holdings via DIRECT's CBIR functionality. The DIRECT service finds the collections in the library and requests the images using the same method that the portal uses to search and request images. DIRECT will perform feature extraction on the images and store the features in its own database. A portal or client application can query DIRECT, and it will use the features of the desired image to search the library for images with the same features. DIRECT will automatically monitor collections in order to keep the DIRECT index up-to-date.
Oriented filters for vessel contrast enhancement with local directional evidence

Vascular structures occur in abundance within biomedical and biological image processing applications. From detecting retinal blood vessels to analyzing shape and connectivity of neurons, segmentation of vascular structures has received significant attention in the literature. Robust segmentation often demands a preprocessing stage involving enhancement of the tubular objects. We propose a novel method to enhance vascular structures from low contrast images by incorporating evidence of neighboring tubular structures in addition to the local vessel detection. We show that the proposed algorithm, called local directional evidence (LDE), is capable of handling bifurcations, intensity inhomogeneity and complex geometry of the vessels, thus providing a robust preprocessing for segmentation. Experiments on a collection of biological images containing vascular objects suggest efficacy of LDE when used as a precursor to segmentation. We observe that LDE improves the average segmentation performance by 63% on our database over the vessel enhancing filter of .
Agglomerative clustering of feature data for image segmentation

We propose an image segmentation model using an agglomerative clustering technique. The clustering is performed within a feature matrix where intensity and boundary relations are defined between neighboring segments. The iterative segment agglomeration process satisfies a cluster aggregation property and does not utilize a priori knowledge of the number of clusters present in the image. The performance of the algorithm is demonstrated on a number of color images and compared with a similar algorithm.
Speckle reducing diffusion for ultrasound image enhancement using the structural similarity image measure

An adaptive approach to enhancing images obtained from an array of ultrasonic transducer elements is proposed and evaluated. The basic algorithm is driven by a system of partial differential equations that 1) reduce speckle by way of the instantaneous (local) coefficient of variation and 2) force congruence with an anatomical model using a well-known perceptual quality metric. A differential form of the quality metric, the structural similarity image measure (SSIM), is derived and applied. This update mechanism registers the image data to the model, thus solving segmentation simultaneously with enhancement. The algorithm, called SSIM diffusion, is tested on a needle placement application in phlebotomy in which delineation of a vessel boundary is required. A group of images obtained from a portable C-scan ultrasonic sensor is used to evaluate the enhancement and segmentation algorithm. Comparisons to a standard speckle reducing diffusion algorithm show that the modelAbased SSIM diffusion superior enhancement with a 67% increase in measurable image quality over the original.
Active contours with area-weighted binary flows for segmenting low SNR imagery

The detection of region boundaries in low SNR images using region based active contours is investigated. In this paper, we first show that the region based active contours models based on the region mean-difference binary flows suffer from two drawbacks when being applied to the detection of low SNR objects: (1) the contours give biased boundary locations if the image is smoothed with a linear filter; (2) the contours detect numerous false boundaries if the image is not prefiltered. Toward this end, we present a model that quantifies the contour location bias. Then, we derive a new region based model to detect the boundaries of objects in low SNR imagery, based on techniques of curve evolution, area-weighted mean-difference binary flows, and level sets. Finally, we present various experimental results to illustrate that the area-weighted technique can overcome the drawbacks of the existing method.
Detection of myocardinal boundaries from ultrasound imagery using active contours

Tracking the motion of the myocardium of the murine heart from ultrasound image sequence aids in the assessment of therapies on human cardiovascular illness. In order to track myocardium, it is necessary to segment both the inner and outer boundaries of the myocardium in each frame. In this paper, we present a snake-based method to the problem of myocardium contouring by exploiting the features of the ultrasound imagery of the mouse heart and speckle reducing anisotropic diffusion. The proposed algorithm features an adaptive snake initialization technique. A sequence of 20 images is tested using the proposed approach and the obtained results are compared with manual outlines of the myocardium. It is shown that the method agrees well qualitatively and quantitatively in terms of the Pratt's figure of merit as stated in W.K. Pratt (1977).
Tubularity Flow Field—A Technique for Automatic Neuron Segmentation

A segmentation framework is proposed to trace neurons from confocal microscopy images. With an increasing demand for high throughput neuronal image analysis, we propose an automated scheme to perform segmentation in a variational framework. Our segmentation technique, called tubularity flow field (TuFF) performs directional regional growing guided by the direction of tubularity of the neurites. We further address the problem of sporadic signal variation in confocal microscopy by designing a local attraction force field, which is able to bridge the gaps between local neurite fragments, even in the case of complete signal loss. Segmentation is performed in an integrated fashion by incorporating the directional region growing and the attraction force-based motion in a single framework using level sets. This segmentation is accomplished without manual seed point selection; it is automated. The performance of TuFF is demonstrated over a set of 2D and 3D confocal microscopy images where we report an improvement of >75% in terms of mean absolute error over three extensively used neuron segmentation algorithms. Two novel features of the variational solution, the evolution force and the attraction force, hold promise as contributions that can be employed in a number of image analysis applications.
Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation

Inhaled hyperpolarized helium-3 (/sup 3/He) gas is a new magnetic resonance (MR) contrast agent that is being used to study lung functionality. To evaluate the total lung ventilation from the hyperpolarized /sup 3/He MR images, it is necessary to segment the lung cavities. This is difficult to accomplish using only the hyperpolarized /sup 3/He MR images, so traditional proton (/sup 1/H) MR images are frequently obtained concurrent with the hyperpolarized /sup 3/He MR examination. Segmentation of the lung cavities from traditional proton (/sup 1/H) MRI is a necessary first step in the analysis of hyperpolarized /sup 3/He MR images. In this paper, we develop an active contour model that provides a smooth boundary and accurately captures the high curvature features of the lung cavities from the /sup 1/H MR images. This segmentation method is the first parametric active contour model that facilitates straightforward merging of multiple contours. The proposed method of merging computes an external force field that is based on the solution of partial differential equations with boundary condition defined by the initial positions of the evolving contours. A theoretical connection with fluid flow in porous media and the proposed force field is established. Then by using the properties of fluid flow we prove that the proposed method indeed achieves merging and the contours stop at the object boundary as well. Experimental results involving merging in synthetic images are provided. The segmentation technique has been employed in lung /sup 1/H MR imaging for segmenting the total lung air space. This technology plays a key role in computing the functional air space from MR images that use hyperpolarized /sup 3/He gas as a contrast agent.