nils-herrmann/clean_scirepeval huggingface dataset:
Texture Classification with Ants

In this paper, we present a novel texture classification algorithm inspired by the self-assembling behavior of real ants when building live structures with their bodies. The proposed algorithm employs dyadic Gabor filter banks for extracting discriminant features from images containing multiple textures not known to the algorithm. The feature space is clustered using the novel ant tree clustering (ATC) algorithm based on the similarity of ants carrying the feature vectors. The results thus obtained show promise of the proposed approach.
Hybrid Diversification Operator-Based Evolutionary Approach Towards Tomographic Image Reconstruction

The proposed algorithm introduces a new and efficient hybrid diversification operator (HDO) in the evolution cycle to improve the tomographic image reconstruction and diversity in the population by using simulated annealing (SA), and the modified form of decreasing law of mutation probability. This evolutionary approach has been used for parallel-ray transmission tomography with the head and lung phantoms. The algorithm is designed to address the observation that the convergence of a genetic algorithm slows down as it evolves. The HDO is shown to yield a higher image quality as compared with the filtered back-projection (FBP), the multiscale wavelet transform, the SA, and the hybrid continuous genetic algorithm (HCGA) techniques. Various crossover operators including uniform, block, and image-row crossover operators have also been analyzed, and the latter has been generally found to give better image quality. The HDO is shown to yield improvements of up to 92% and 120% when compared with FBP in terms of PSNR, for 128 &times; 128 head and lung phantoms, respectively.
A novel framework for exploratory analysis of highly variable morphology of migrating epithelial cells

Migratory cells, for example human retinal epithelial (RPE) cells, exhibit highly variable morphology. This makes it difficult to use traditional methods, such as the landmark based Procrustes analysis or feature based analysis, to quantitatively represent their shapes. We propose a novel framework to generate a low-dimensional representation of highly variable cell shapes. The framework lends itself readily to efficient exploratory analysis of a given cell shape dataset in order to visualise morphological trends in the data and reveal the intrinsic structure of various morphology-based cell phenotypes in the data. Preliminary results show that the framework is effective in revealing consistent morphological phenotypes.
Surface Estimation and Tracking using Sequential MCMC Methods for Video Based Rendering

Video based rendering algorithms attempt to render videos of a scene from an arbitrary viewpoint, given a set of input video sequences taken from several fixed viewpoints. These algorithms require either a dense camera array or some knowledge of scene structure. By applying sequential Markov Chain Monte Carlo (MCMC) methods, we show it is possible to estimate the surfaces visible within a scene, and track them over time, in an efficient manner. Initially, a particle filter is applied across image scale to estimate the surfaces present in a scene at a fixed point in time. Following this, surfaces are tracked over time using a particle filter which takes advantage of both frame-to-frame dependancies, and a hierarchical surface model derived from a multiresolution Gaussian mixture model analysis of the surface data. This time-varying surface model, and the images, are the input for a rendering algorithm which uses a fuzzy z-buffer and projective texturing to generate reconstructions.
Handcrafted features with convolutional neural networks for detection of tumor cells in histology images

Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.
Adaptive wavelet packet basis selection for zerotree image coding

Image coding methods based on adaptive wavelet transforms and those employing zerotree quantization have been shown to be successful. We present a general zerotree structure for an arbitrary wavelet packet geometry in an image coding framework. A fast basis selection algorithm is developed; it uses a Markov chain based cost estimate of encoding the image using this structure. As a result, our adaptive wavelet zerotree image coder has a relatively low computational complexity, performs comparably to state-of-the-art image coders, and is capable of progressively encoding images.
Classification of potential nuclei in prostate histology images using shape manifold learning

The demanding step in the development of ancillary systems for the diagnosis of cancer and other diseases based on nuclear morphometry is the delineation of nuclei in the images of stained tissue sections. Various constituents of the tissue section such as cellular and extra-cellular elements, staining artefacts, debris of nuclei, and clusters of overlapping nuclei apart from the image acquisition noise to name a few contribute to in the complexity of the task. In this paper, we pose the problem of selection of nuclei in tissue section as classification of shapes using manifold learning on training images followed by out-of-sample extension for unknown test images. Experimental results demonstrate the effectiveness of the proposed algorithm.
Stack-run adaptive wavelet image coding

An adaptive wavelet transform based on an image coder that employs a stack-run representation for quantized transform coefficients and benefits from the intra-subband redundancies is presented. The compression algorithm can be divided into four parts. First, an adaptive wavelet packet basis is selected for representing the given image using certain entropy-based cost functions. Second, the wavelet packet coefficients are quantized using an optimal scalar quantizer for Laplacian distribution. Third, the quantized coefficient is represented with stack-run coding generating a redundant symbol stream. Finally, this symbol stream is entropy coded using a higher order arithmetic coder.
Video Based Rendering using Surfaces Patches

This paper describes a novel method to perform video based rendering. By capturing a set of real video sequences of a scene, the aim is to render a video sequence, in real time, from any viewpoint. By modelling the surfaces of a scene as a set of disjoint planar patches, we are able to efficiently estimate the parameters of the scene geometry. The patches can then be tracked over time using a multiresolution hierarchy. This time-varying surface model, and the images, are the input for the rendering algorithm, which uses a fuzzy z-buffer and projective texturing to generate reconstructions.
Less redundant codes for variable size dictionaries

We report on a family of variable-length codes with less redundancy than the flat code used in most of the variable size dictionary-based compression methods. The length of codes belonging to this family is still bounded above by [log/sub 2/ |D|] where |D| denotes the dictionary size. We describe three of these codes, namely, the balanced code, the phase-in-binary code (PB), and the depth-span code (DS). As the name implies, the balanced code is constructed by a height balanced tree, so it has the shortest average codeword length. The corresponding coding tree for the PB code has an interesting property that it is made of full binary phases, and thus the code can be computed efficiently using simple binary shifting operations. The DS coding tree is maintained in such a way that the coder always finds the longest extendable codeword and extends it until it reaches the maximum length. It is optimal with respect to the code-length contrast. The PB and balanced codes have almost similar improvements, around 3% to 7% which is very close to the relative redundancy in flat code. The DS code is particularly good in dealing with files with a large amount of redundancy, such as a running sequence of one symbol. We also did some empirical study on the codeword distribution in the LZW dictionary and proposed a scheme called dynamic block shifting (DBS) to further improve the codes' performance. Experiments suggest that the DBS is helpful in compressing random sequences. From an application point of view, PB code with DBS is recommended for general practical usage.
Multilateral filtering: A novel framework for generic similarity-based image denoising

We present a novel iterative nonlinear filtering framework, termed multilateral filtering, based on the idea of generic local similarity. A set of local features is computed for each pixel using its local neighborhood. Two pixels are considered to be similar if the Euclidean distance between their corresponding feature vectors is small and vice versa. Multilateral filtering results in image smoothing while preserving edge and textural features. Our experimental results show that the proposed method produces comparable and often better results than the state-of-the-art denoising methods.
Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data
A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.
Adaptive wavelet restoration of noisy video sequences

In this paper, we present a novel algorithm for restoration of noisy video sequences. A video sequence is first transformed into an optimal 3D wavelet domain using basis functions adapted to the contents of the sequence. Assuming that all the major spatiotemporal frequency phenomena present in the sequence produce high amplitude transform coefficients, a modified form of the BayesShrink thresholding method is used to suppress the noise. In order to reduce the effects of Gibbs phenomenon in the restored sequence, translation dependence is removed by averaging the restored instances of the shifted sequence. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.
A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.
A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

In this paper, we present a stochastic model for glandular structures in histology images of tissue slides stained with Hematoxylin and Eosin, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and a likelihood for the presence of a glandular structure. The inference is made via a Reversible-Jump Markov chain Monte Carlo simulation. To the best of our knowledge, all existing published algorithms for gland segmentation are designed to mainly work on healthy samples, adenomas, and low grade adenocarcinomas. One of them has been demonstrated to work on intermediate grade adenocarcinomas at its best. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular structures in histology images of normal human colon tissues as well as benign and cancerous tissues, excluding undifferentiated carcinomas.
Radon/ridgelet signature for image authentication

In this paper, we describe a novel content-based image signature for authentication using the ridgelet transform. The signature is extracted from the Radon domain and entropy coded after a 1D wavelet transform, which is essentially the so-called "ridgelet transform". Unlike traditional authentication signatures, it has the ability to localise tampering at a high resolution, and is robust to content-preserving manipulations such as compression and allows a progressive authentication.
Texture Segmentation using Ant Tree Clustering

Motivated by the self-assembling behavior of real ants, we present a novel algorithm for texture segmentation which is based on ant tree clustering of wavelet features. In a pattern recognition setting, wavelet features are extracted using either of the two subband filtering methods: discrete wavelet transform (DWT) or discrete wavelet packet transform (DWPT). The feature classification process is inspired by the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants thus building trees based on the similarity of feature vectors. The results thus obtained compare favorably to those of other recently published filtering based texture segmentation algorithms
Stain deconvolution of histology images via independent component analysis in the wavelet domain

With the ubiquity of digital slide scanners, histology image analysis is rapidly emerging as an active area of research. Several histology image analysis algorithms such as those for mitotic cell detection, nuclei segmentation and hormone receptors scoring depend on colour information obtained from images of the scanned slides. However, different standards followed by different labs and the technical variation among different scanners result in stain inconsistency in histology images. Thus, applications that use colour information may fail when they are applied to images with different appearance of stain colours. In this paper, we propose a novel method to estimate the so called stain matrix via independent component analysis in the wavelet domain for stain deconvolution in histology images. Experimental results demonstrate stable and more accurate stain deconvolution results as compared to other recently proposed algorithms.
Model based optimal bit allocation

In this paper, analytical modelling of the operational R-D characteristics that can significantly reduce the computational complexity of an optimal bit allocation algorithm is studied. Two new models namely average distortion-rate function and the low bit rate model are also proposed. Comparative results for operational R-D curve fitting using these two models are presented. Preliminary results show that the exponential-polynomial model often gives a better fit than all the other models studied. Model based bit allocation algorithm employed in an image coding framework gives promising results for overall visual quality.
On zerotree quantization for embedded wavelet packet image coding

Wavelet packets are an effective representation tool for adaptive waveform analysis of a given signal. We first combine the wavelet packet representation with zerotree quantization for image coding. A general zerotree structure is defined which can adapt itself to any arbitrary wavelet packet basis. We then describe an efficient coding algorithm based on this structure. Finally, the hypothesis for prediction of coefficients from coarser scale to finer scale is tested and its effectiveness is compared with that of zerotree hypothesis for wavelet coefficients.
Texture based classification of hyperspectral colon biopsy samples using CLBP

Computer aided diagnosis (CAD) is aimed at supporting the pathologists in their diagnosis. In this paper, we present an algorithm for texture-based classification of colon tissue patterns. In this method, a single band is selected from its hyperspectral cube and spatial analysis is performed using circular local binary pattern (CLBP) features. A novel method for feature selection is presented resulting in the best feature set without actually running the classifier. Classification results using Gaussian kernel SVM, with an accuracy of 90%, demonstrate that texture analysis based on CLBP features is able to distinguish the benign and malignant patterns.
A connectivity difference measure for identification of functional neuroimaging markers for epilepsy

Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional MRI (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminative neuroimaging markers. In this paper, we propose a two-stage algorithm to identify functional connectivity differences that can discriminate epileptic patients and healthy subjects. In the first stage, we determine the functional connectivity matrix between brain cortical regions for identification of potentially discriminative neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminative connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 81.33% on unseen dataset. The results demonstrate that the proposed algorithm is capable of determining functional connections between brain regions which aid in discrimination of epileptic patients versus healthy subjects.
MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images

We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation.
A novel system for scoring of hormone receptors in breast cancer histopathology slides

Grading of breast cancer is often done by an expert pathologist based on their analysis of micro-level structural features of the cancerous tissue specimen as well as the level of presence of certain protein molecules in the specimen. The process of assessment of the level of presence of estrogen and progesterone receptors molecules is subjective by its very nature and therefore, causes large inter-expert and sometimes even intra-expert variability, potentially adding noise to the process of selecting the treatment regime for the patient. Quantification of immunohistochemical stains is critical for an objective assessment of breast cancer histopathology specimens. We present a fast, compact and inexpensive system for scoring the Estrogen and Progesterone hormone receptors in breast cancer histopathology slides using image analysis algorithm. We describe hardware and software issues in the construction of the system, and present a comparison of scores produced by our system to those produced by many expert pathologists.