nils-herrmann/clean_scirepeval huggingface dataset:
Connected filtering by graylevel classification through morphological histogram processing

The paper introduces a new connected filter which provides image simplification in terms of flat zone and graylevel reduction. This filtering consists of the application of morphological operators to the histogram. It is done because each object in the image has a significative distribution of graylevels in the histogram. In other words, it is enough to classify the distributions in the histogram for simplification of those objects. The article also shows some experimental results, where the proposed method is compared to other classical methods applied to flat zone and graylevel reduction.
Morphologic analysis of the endothelium corneal tissue using mathematical morphology

An effective method for the analysis of donated corneas consists in the evaluation of the indexes of cellular density, pleomorphism and polimegatism. We have developed a system attached to the slit lamp and dedicated software for image treating and analysis of the referred indexes using the mathematical morphology procedure. The results of automatic determination of the cellular density has a correlation coefficient of 0.95798 compared to measurements done by the interactive counting method and for the polimegatism and pleomorphism indexes the preliminary visual results are in good agreement with the presented processed image.
Hands-on digital image processing

This effort presented is the integration of the Khoros 2 system, hypertext (HTML), and network browsers to produce an interactive digital image processing environment to enhance our ability to learn and work together across geographical, temporal, and cultural distances. The paper describes how this new type of course can impact teaching, learning, and research processes, and gives examples of how it is currently being used in several settings. The broad scope of the Khoros system and the communal emphasis on collaboration have contributed to this success. This new interactive toolbox/course is available free of charge via the Internet at http://www.khoros.unm.edu/dipcourse.
Silver standard masks for data augmentation applied to deep-learning-based skull-stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the large number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step.
Tie-Zone Watershed, Bottlenecks, and Segmentation Robustness Analysis

In a recent paper , a new type of watershed (WS) transform was introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique (and thereby unbiased) optimal solution. Indeed, many optimal solutions are sometimes possible when segmenting an image by WS. The TZWS assigns each pixel to a catchment basin (CB) if in all solutions it belongs to this CB. Otherwise, the pixel is said to belong to a tie-zone (TZ). An efficient algorithm computing the TZWS and based on the Image Foresting Transform (IFT) was also proposed. In this article, we define the new concept of "bottlenecks" in the watermerging paradigm. Intuitively, the bottlenecks are the first contact points between at least two different wave fronts. They are pixels in the image where different colored waters meet and tie and from which may begin, therefore, the tie-zones. They represent the origin points or the access of the tie-zones (regions that cannot be labeled without making arbitrary choices). If they are preferentially assigned to one or another colored water according to an arbitrary processing order, as occurs in most of watershed algorithm, an entire region (its influence zone - the "bottle"!) is conquered together. The bottlenecks play therefore an important role in the bias that could be introduced by a WS implementation. It is why we show in this paper that both tie-zones and bottlenecks analysis can be associated with the robustness of a segmentation.
Toolbox of image processing using the Python language

This work consists in the study, development and implementation of a toolbox for image processing using the Python language and the numerical Python package. This set has "open source" distribution and is adequate for multidimensional mathematical processing. Python is a modern and well projected language, interpreted, "very-high-level", object oriented and extremely portable, apart from being suitable for rapid application development (RAD). The system is generated using the methodology of the Adesso project for construction of scientific software. This environment is useful in education, research and development of final applications.
A corneal endothelium cell analyzer for slit lamps

An optical system that attaches to a slit lamp is an alternative to the cost and limitations of specular microscopes. In order to overcome the limitations of subjective assessment of all the endothelial layers, the difficulties of manipulation, high cost, and analysis of several parts of the cornea, we have developed a system that can be attached to the slit lamp (290/spl times/ nominal magnification of the image of the cells displayed on a PC monitor and a dedicated software for image treating and cells counting). Results are in good agreement with measurements done in specular microscopes (correlation coefficient is 0.98081) and the automatic counting method is also in good agreement with the interactive counting method (correlation coefficient is 0.95798).
Segmentation of Brain Structures by Watershed Transform on Tensorial Morphological Gradient of Diffusion Tensor Imaging

Watershed transform on tensorial morphological gradient (TMG) is a new approach to segment diffusion tensor images (DTI). Since the TMG is able to express the tensorial dissimilarities in a single scalar image, the segmentation problem of DTI is then reduced to a scalar image segmentation problem. Therefore, it can be addressed by well-known segmentation techniques, such as the watershed transform. In other words, by computing the TMG of a DTI, and then using the hierarchical watershed transform, it is possible to segment brain structures, such as the corpus callosum, the ventricles and the cortico-spinal tracts, and use the results for subsequent quantitative analysis of DTI parameters. Experiments showed that segmentations obtained with the proposed approach are similar to the ones obtained by other segmentation techniques based on DTI and also segmentation methods based on other Magnetic Resonance Imaging (MRI) modalities. Since the proposed method, as opposed to the majority of the DTI based segmentation methods, does not require manual seed and/or surface placement, its results are highly repeatable. And unlike other methods that have sometimes four parameters to be adjusted, the only adjustable parameter is the number of regions in which the image should be segmented, making it simple and robust.
Extended extinction profile for the classification of hyperspectral images

In this paper, a novel approach is proposed for the spectral-spatial classification of hyperspectral images. The proposed classification approach is based on a novel filtering technique, here entitled as extended extinction profile (EEP). The proposed classification approach is applied on two well-known data sets: Pavia University and Indian Pines; and the obtained results have been compared with one of the strongest filtering approaches in the literature named extended attribute profile (EAP). Results confirm that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images.
Sulcal lines extraction for cortex characterization in cerebral MR images using the image foresting transform

We describe an approach to extract sulcal lines from cerebral MRI data using the image foresting transform. This technique, based on graph theory, provides a sound framework for the implementation of many image processing operators, and has been successfully applied to the solution of many problems, including some in medical imaging. We show the results of applying the technique to slices of MRI data from four subjects and compare these to the computation of the morphological skeleton and to the conditional thickening. A shorter version has already been submitted to ICIP 2003, but the comparison to the conditional thickening has only been presented. Also, we present a correction to the IFT procedure which prevents the sulcal lines from breaking.
Local Scale for Boundary Shape Description: Application in Locating Landmarks Automatically

A good shape descriptor is necessary for automatically identifying landmarks on boundaries. Our method of boundary shape description is based on the notion of c- scale, which is a new local scale concept, defined at each boundary element. From this representation we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. The results show that this method gives a complete description of shape and allows the automatic positioning of mathematical landmarks, which agree with our intuitive ideas of where landmarks may be defined. This method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.
The image foresting transform: theory, algorithms, and applications

The image foresting transform (IFT) is a graph-based approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it-a generalization of Dijkstra's algorithm-with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.
Interactive 3D ScanningWithout Tracking

Using inexpensive and readily available materials-a calibrated pair of cameras and a laser line projector - a 3D laser range scanner which requires no tracking is implemented in this paper. We introduce a planarity constraint for reconstruction, based on the fact that all points observed on a laser line in an image are on the same plane of laser light in 3D. This plane of laser light linearly parametrizes the homography between a pair of images of the same laser line, and this homography can be recovered from point correspondences derived from epipolar geometry. Points visible from multiple views can be reconstructed via triangulation and projected onto this plane, while points visible in only one view can be recovered via ray-plane intersection. The use of the planarity constraint during reconstruction increases the system's accuracy, and using the planes for reconstruction increases the number of points recovered. Additionally, an in teractive scanning environment is constructed, where incremental reconstruction is used to provide continuous visual feedback. Reconstructions with this method are shown to have higher accuracy than standard triangulation.
New Extinction Values from Efficient Construction and Analysis of Extended Attribute Component Tree

A gray-level image can be interpreted as a topographical surface, and represented by a component tree, based on the inclusion relation of connected components obtained by threshold decomposition. Relations between plateaus, valleys or mountains of this relief are useful in computer vision systems. An important definition to characterize the topographical surface is the dynamics, introduced by Grimaud (1992), associated to each regional minimum. This concept has been extended, by Vachier and Meyer (1995), by the definition of extinction values associated to each extremum of the image. This paper proposes four new extinction values -- two based on the topology of the component tree: (i) number of descendants and (ii) sub-tree height; and two geometric: (iii) height and (iv) width of a level component bounding box. This paper describes efficient computation of these extinction values based on the incremental determination of attributes from the component tree construction in quasi-linear time, compares the computation time of the method and illustrates the usefulness of these new extinction values from real examples.
Reliability of using single specialist annotation for designing and evaluating automatic segmentation methods: A skull stripping case study

Manual annotation by specialists has been largely used as a gold standard for evaluation of automatic segmentation methods in medical imaging and also for designing datadriven approaches. The use of single-specialist manual annotation in this context may lead to a poor assessment and/or training of automatic segmentation methods. This work investigates the limitations of using single-specialist manual annotation as a gold standard. This investigation is framed within a common case study: application of skull stripping to magnetic resonance (MR) images. Twelve MR volumes acquired with scanners of different vendors and at different magnetic field strengths were manually annotated. Each volume was annotated twice. Nine publicly available automatic skull stripping methods were applied to these twelve volumes. The Dice coefficient overlap metric was used to rank the automatic skull stripping methods. Our results show differences (p < 0.05) that depended on the manual annotation reference used to compute the metrics. The ranking of the best skull stripping methods changed with the gold standard. Manual annotation also plays a fundamental role in data-driven approaches, such as convolutional neural networks (CNNs). An auto-context CNN was evaluated using two public datasets in addition to our twelve volumes, to assess manual annotation influence both during training and validation of the algorithm. Our results indicate that the CNN has a strong bias towards the manual annotation used during training, potentially resulting in poor generalization of the model. These results highlight the importance of having robust gold standards, and raise the need for developing better methodologies for generating gold standards.
Morphological operators for segmentation of color sequences

This paper presents a technique for the segmentation of moving objects in color image sequences. The technique is based on Beucher-Meyer paradigm, with markers detected by a morphological operator designed by computational learning (or, equivalently, statistical optimization). Objects in some frames of the video are marked manually and used to train the markers detector. Then, the operator designed is used to mark the objects in the other frames and the paradigm is applied to all frames marked by the detector. Two real world examples illustrate the application of the proposed technique.
Segmentation of images of yeast cells by scale-space analysis

We present an hierarchical approach to segment images of yeast cells based on watershed and space-scale analysis. Yeasts belong to an important fungi class and the performance of bioreactors and other chemical processes are greatly influenced by their morphological character. The method proposed is capable of segmenting yeast cells based on the analysis of survival time, shape and gray-scale features of the Tree of Critical Lakes. We show experimental results for one group of yeast images obtained from the School of Food Engineering at Unicamp, Brazil. Preliminary comparison shows that the proposed method provides cells with area 10% lower than traditional approach, as well as its contours preserved.
Classification of the distance transformation algorithms under the mathematical morphology approach

The distance transform (DT) is a morphological erosion of the binary image by a given structuring function, that dictates the distance metric in the transformation. There are many known algorithms and structuring function decompositions to efficiently implement a morphological erosion. Most of the erosion algorithms are classified as parallel, sequential raster (and anti-raster), and propagation. Based on this classification and decomposition, we review and classify most of the DT algorithms reported in the literature. As a result of this study, we have contributed not only to better classify and understand the diversity of the DT algorithms in the literature, but also to create a collection of efficient erosion algorithms suitable to different computer architectures.
The tie-zone watershed: definition, algorithm and applications

In this work, a new type of watershed transform is introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique and optimal solution. Indeed, many solutions are sometimes possible when segmenting an image with a watershed algorithm. In this case, the TZWS assigns each pixel to a catchment basin (CB) if in all solutions it belongs to this CB. Otherwise, the pixel is said to belong to a tie-zone (TZ). We propose an efficient algorithm based on image foresting transform (IFT) which computes the TZWS transform as a shortest-path forest. Finally, two applications of this TZWS are presented: bounding intervals for segmented objects' extensions and a progressive segmentation procedure.
Classification of microstructures by morphological analysis and estimation of the hydration degree of cement paste in concrete

Cement is an essential material for the development of the civil construction. The analysis of cement-based materials is useful in the evaluation of mechanical properties, water absorption, air permeability and evolution of hydration degree. The latter is the motivation of this paper. New advantageous techniques of analysis based on the image information are possible using electronic microscopy. We propose here an alternative procedure to the segmentation of microstructures of cement and aggregates in concrete. The central idea consists of a particular use of the morphological operator watershed. We report the results of the analysis of about 450 scanning electron microscope (SEM) images.
Characterization of the human cortex in MR images through the image foresting transform

In this work we discuss an approach to characterize the human cortex in MRI data through the segmentation of the cortical layer with deep penetration into the sulci, using the Image Foresting Transform. This technique, based on graph theory, provides a sound framework for the implementation of many image processing operators, and has been successfully applied to the solution of many problems, including some in medical imaging. We show the results of applying the technique to slices of MRI data from four subjects and compare these to the computation of the morphological skeleton.
Interactive max-tree visualization tool for image processing and analysis

The max-tree is a data structure that represents all possible upper thresholds of an image, it has been successfully used in many image processing and analysis applications. The max-tree corresponding to a natural image usually has thousands of nodes, which makes unpractical to build a comprehensive graphical representation of its complete structure. In this paper, we propose a methodology that allows to build an interactive max-tree graphical representation that permits the user to navigate through the max-tree nodes, to visualize its connected components and to create node subsets. Our representation displays a simplified max-tree, but it allows the user to access all max-tree nodes using the interactive features. To the best of our knowledge, this is the first work that proposes an interactive graphical representation of the max-tree. We depict the potential of our max-tree visualization tool for interactive segmentation, connected filtering, and collection of training samples.
An array-based node-oriented max-tree representation

This paper presents an array-based node-oriented structure for the max-tree representation, which allows direct access and flexible manipulation of its nodes, and is more suitable for OpenMP parallel processing. The proposed structure is based on two arrays called node array (NA), which stores attributes of the nodes, and node index (NI), which indicates the node that each pixel belongs to. We compare it with the pixel-oriented max-tree representation based on a parent array (parent) and an ordering array (S) that allows tree traversals. We show that our max-tree representation requires less memory when the ratio between the number of image pixels and max-tree nodes is greater than 1.6, which is often the case. It is more flexible, and can compute some attributes, such as height and dynamics, with a complexity linear on the number of max-tree nodes instead of the number of image pixels. In our experiments our structure computed the height attribute on average 11.4 faster than the parent/S representation. Also, for a single area-open filter, the sequential implementation of our structure is on average 1.14 times slower and the parallel implementation in a 4-core CPU is 1.2 times faster than the parent/S structure. For an area-open filter followed by the hmax filter, our sequential implementation is 1.34 times faster and our parallel implementation is 2.32 times faster than the parent/S structure.
Fingerprint Liveness Detection Using Convolutional Neural Networks

With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples-a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.