Removal Of False Positives Research Articles

Crater Detection and Population Statistics in Tianwen-1 Landing Area Based on Segment Anything Model (SAM)

Crater detection is useful for research into dating a planetary surface’s age and geological mapping. The high-resolution imaging camera (HiRIC) carried by the Tianwen-1 rover provides digital image model (DIM) datasets with a resolution of 0.7 m/pixel, which are suitable for detecting meter-scale craters. The existing deep-learning-based automatic crater detection algorithms require a large number of crater annotation datasets for training. However, there is currently a lack of datasets of optical images of small-sized craters. In this study, we propose a model based on the Segment Anything Model (SAM) to detect craters in Tianwen-1’s landing area and perform statistical analysis. The SAM network was used to obtain a segmentation mask of the craters from the DIM images. Then non-circular filtering was used to filter out irregular craters. Finally, deduplication and removal of false positives were performed to obtain accurate circular craters, and their center’s position and diameter were obtained through circular fitting analysis. We extracted 841,727 craters in total, with diameters ranging from 1.57 m to 7910.47 m. These data are useful for further Martian crater catalogs and crater datasets. Additionally, the crater size–frequency distribution (CSFD) was also analyzed, indicating that the surface ages of the Tianwen-1 landing area are ~3.25 billion years, with subsequent surface resurfacing events occurring ~1.67 billion years ago.

Evaluating automated longitudinal tumor measurements for glioblastoma response assessment.

Automated tumor segmentation tools for glioblastoma show promising performance. To apply these tools for automated response assessment, longitudinal segmentation, and tumor measurement, consistency is critical. This study aimed to determine whether BraTumIA and HD-GLIO are suited for this task. We evaluated two segmentation tools with respect to automated response assessment on the single-center retrospective LUMIERE dataset with 80 patients and a total of 502 post-operative time points. Volumetry and automated bi-dimensional measurements were compared with expert measurements following the Response Assessment in Neuro-Oncology (RANO) guidelines. The longitudinal trend agreement between the expert and methods was evaluated, and the RANO progression thresholds were tested against the expert-derived time-to-progression (TTP). The TTP and overall survival (OS) correlation was used to check the progression thresholds. We evaluated the automated detection and influence of non-measurable lesions. The tumor volume trend agreement calculated between segmentation volumes and the expert bi-dimensional measurements was high (HD-GLIO: 81.1%, BraTumIA: 79.7%). BraTumIA achieved the closest match to the expert TTP using the recommended RANO progression threshold. HD-GLIO-derived tumor volumes reached the highest correlation between TTP and OS (0.55). Both tools failed at an accurate lesion count across time. Manual false-positive removal and restricting to a maximum number of measurable lesions had no beneficial effect. Expert supervision and manual corrections are still necessary when applying the tested automated segmentation tools for automated response assessment. The longitudinal consistency of current segmentation tools needs further improvement. Validation of volumetric and bi-dimensional progression thresholds with multi-center studies is required to move toward volumetry-based response assessment.

Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Cloud Shadow Detection via Ray Casting with Probability Analysis Refinement Using Sentinel-2 Satellite Data

Analysis of aerial images provided by satellites enables continuous monitoring and is a central component of many applications, including precision farming. Nonetheless, this analysis is often impeded by the presence of clouds and cloud shadows, which obscure the underlying region of interest and introduce incorrect values that bias analysis. In this paper, we outline a method for cloud shadow detection, and demonstrate our method using Canadian farmland data obtained from the Sentinel-2 satellite. Our approach builds on other object-based cloud and cloud shadow detection methods that generate preliminary shadow candidate masks which are refined by matching clouds to their respective shadows. We improve on these components by using ray-casting and inverse texture mapping methods to quickly identify cloud shadows, allowing for the immediate removal of false positives during image processing. Leveraging our ray-casting-based approach, we further improve our results by implementing a probability analysis based on the cloud probability layer provided by the Sentinel-2 satellite to account for missed shadow pixels. An evaluation of our method using the average producer (82.82%) and user accuracy (75.55%) both show a marked improvement over the performance of other object-based methods. Methodologically, our work demonstrates how incorporating probability analysis as a post-processing step can improve the generation of shadow masks.

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

High-Throughput Association Mapping in Brassica napus L.: Methods and Applications.

Oil seed rape (Braasica napus L.) is ranked second among oil seed crops cultivated globally for edible oil for human, and seed cake for animal consumption. Recent genetic and genomics advancements highlighted the diversity that exists within B. napus, which is largely discovered using the most promising genetic markers called single nucleotide polymorphism (SNP). Their calling rate is also enhanced to ~100 folds after the continuous advancements in the next generation sequencing (NGS) technologies. As the high throughput of NGS resulted in multi-Giga bases data, the detailed quality control (QC) prior to downstream analyses is a pre-requisite. It mainly involved the removal of false positives, missing proportions, filtering of low-quality SNPs, and adjustments of minor-allele frequency and heterozygosity. After marker-trait association, for conformation of target SNPs, validations of SNPs can be performed using various methods, especially allele-specific PCR assay-based methods have been utilized for SNP genotyping of genes targeting agronomic traits and somaclonal variations occurred during transgenic studies. In the present study, the authors mainly argue on the genotypic progress, and pipelines/methods that are being used for detection, calling, filtering, and validation of SNPs. Also, insight is provided into the application of SNPs in linkage and association mapping, including QTL mapping and genome-wide association studies targeting mainly developmental traits related to the root system and plant architecture, flowering time, silique, and oil quality. Briefly, the present study provides the recent information and recommendations on the SNP genotyping methods and its applications, which can be useful for marker-assisted breeding in B. napus and other crops.

Natural language processing-optimized case selection for real-world evidence studies.

1556 Background: Much information describing a patient’s cancer treatment remains in unstructured text in electronic health records and is not recorded in discrete data fields. Accurate data completeness is essential for quality care improvement and research studies on de-identified patient records. Accessing this high-value content often requires manual and extensive curation review. Methods: AstraZeneca, CancerLinQ, ConcertAI, and Tempus have developed a natural language processing (NLP)-assisted process to improve clinical cohort selection for targeted curation efforts. Hybrid, machine-learning model development included text classification, named entity recognition, relation extraction and false positive removal. A subset of nearly 60,000 lung cancer cases were included from the CancerLinQ database, comprised of multiple source EHR systems. NLP models extracted EGFR status, stage, histology, radiation therapy, surgical resection and oral medications. Based on the results, cases were selected for additional manual curation, where curators confirmed findings of the NLP-processed data. Results: NLP methods improved cohort identification. Successfully returned cases using the NLP method ranged from 75.2% to 96.5% over more general case selection criteria based on limited structured data. For all cohorts combined, 84.2% of the cases sent out for NLP curation were returned with curated content (Table). Each cohort contained a range of NLP-derived elements for curators to further review. In comparison, more general case selection criteria yielded a total of 3,878 cases returned out of 41,186 lung cancer cases sent for curation, for a success rate of only 9.6%. Conclusions: NLP-driven case selection of six distinct, complex lung cohorts resulted in an order of magnitude improvement in eligibility over candidate selection using structured EHR data alone. This study demonstrates NLP-assisted approaches can significantly improve efficiency in curating unstructured health data. [Table: see text]

Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.

Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy. This paper focuses on the segmentation of red lesions using nested U-Net Zhou et al. (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2018)followed by removal of false positives based on the sub-image classification method. Different sizes of sub-images were studied for the reduction in false positives in the sub-image classification method. The network could capture semantic features and fine details due to dense convolutional blocks connected via skip connections in between down sampling and up sampling paths. False-negative candidates were very few and the sub-image classification network effectively reduced the falsely detected candidates. The proposed framework achieves a sensitivity of [Formula: see text], precision of [Formula: see text], and F1-Score of [Formula: see text] for the DIARETDB1 data set Kalviainen and Uusutalo (Medical Image Understanding and Analysis, Citeseer, 2007). It outperforms the state-of-the-art networks such as U-Net Ronneberger et al. (International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015)and attention U-NetOktay et al. (Attention u-net: Learning where to look for the pancreas, 2018).

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.

Cloning detection scheme based on linear and curvature scale space with new false positive removal filters

Cloning detection scheme based on linear and curvature scale space with new false positive removal filters

MAST (Movement Analysis Software for Telemetry data), for the semi-automated removal of false positives from radio telemetry data

IntroductionRadio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed MAST (Movement Analysis Software for Telemetry data) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.MethodsMAST uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested MAST on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. MAST has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. MAST also assesses concordance with a traditional consecutive detection filter using Cohen’s κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}.ResultsOverall MAST performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. MAST classified between 94 and 99% of study tag detections as valid.ConclusionAs part of a robust data management plan, MAST is able to discriminate between detections from study tags and known false positives. MAST works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. MAST provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.

Biodiversity Heritage Library and Global Names: Successes, opportunities and the challenges for the future collaboration

The Biodiversity Heritage Library (BHL) is a major aggregator of biodiversity literature with more than 200,000 volumes. The Global Names Architecture (GNA) strives to develop and provide tools for finding, parsing and verifying scientific names. GNA and BHL have enjoyed 10 years of collaboration in the creation of a scientific names index for BHL. Such an index provides researchers with a means for finding data about more than a million species. Recently, BHL and GNA developed a workflow for the creation of an index that covers more than 50 million pages of BHL, and finds and verifies scientific names in less than a day. The unprecedented speed of the index creation opens an opportunity to dramatically increase its quality and reach. The following challenges can now be addressed. 1. Abbreviated names reconciliation. From 20% to 25% of all scientific names in BHL are abbreviated. It is much harder to reconcile and verify abbreviated names, because their specific epithets are not unique. We plan to reconcile the vast majority of such names via a statistical approach. 2. Linking of biodiversity publication titles with actual pages in BHL. Scientific names are closely connected to publications of original description, taxonomic treatments, and other usages. We plan to build algorithms for finding out how different lexical variants of the same publication reference can be disambiguated and connected to corresponding BHL pages. 3. Using taxonomic intelligence for finding information about species. According to our estimation, on average, there are three scientific names (historical and current) per taxon. Names of species often change over time as a result of misspellings, and homotypic or heterotypic synonymy. We plan to link outdated names with currently accepted names of taxa. This functionality provides all information about a taxon in BHL, no matter what names were used to reference the taxon at the time of publication. 4. Finding information about original descriptions of genera and species. For every species there is a publication with the original description. We want to create an index of species that are described in the publications aggregated by BHL. 5. Detection of species names in spite of "incorrect" capitalization. Previously, or in horticultural sources, specific epithets were often capitalized (e.g., Bulbophyllum Nocturnum), or for patronyms in which the species was named in honor of someone (e.g., Notiospathius Johnlennoni). We plan to detect names with non-standard capitalization of this sort. 6. Removal of false positives. Texts in Latin language, names of people, and geographical entities often create false positives that look like scientific names. Using machine learning techniques will allow us to detect and remove most of these errors from the names index. 7. Detection of the names of biodiversity scientists and geographical entities in texts. Finding names of biologists and geographical places in addition to scientific names would allow us to draw connections between these data and to create metadata demonstrating these links. We plan to add tools and algorithms for indexing person names and geographical names. In this talk I will present plans for a dramatic quality increase in the scientific name-finding algorithms, as well as an introduction of other elements that would enhance usability of BHL for its patrons.

Monitoring the phenology of the wood frog breeding season using bioacoustic methods

The breeding phenology of wood frogs (Lithobates sylvaticus) in subarctic ecosystems is closely tied to the weather conditions at the onset of spring. With climate change occurring at an accelerated rate at high latitudes, we expect changes in the timing of the onset of spring to impact the timing of wood frog breeding. Wood frogs in the subarctic emerge from hibernation just after the snow melts and ambient air temperatures increase, but local and regional drivers of breeding phenology are not well understood and are difficult to study due to the relative inaccessibility of the region. We deployed passive acoustic monitors at multiple sites from 2011 to 2017 to track the timing of wood frog breeding based on calling activity and determine how this activity relates to environmental conditions. We developed a semi-automated approach for detecting wood frog calls, including the manual removal of false positives, and show that these estimates of activity are closely related to the timing of snowmelt and ambient air temperature. Our results suggest that wood frog breeding phenology in subarctic and arctic ecosystems will likely change in response to warming conditions, and we conclude that passive acoustic monitoring paired with semi-automated detection of wood frog calling provides an accurate and efficient method for tracking changes in population-scale wood frog breeding in this challenging and rapidly changing ecosystem.

Blessing of dimensionality at the edge and geometry of few-shot learning

In this paper we present theory and algorithms enabling classes of Artificial Intelligence (AI) systems to continuously and incrementally improve with a priori quantifiable guarantees – or more specifically remove classification errors – over time. This is distinct from state-of-the-art machine learning, AI, and software approaches. The theory enables building few-shot AI correction algorithms and provides conditions justifying their successful application. Another feature of this approach is that, in the supervised setting, the computational complexity of training is linear in the number of training samples. At the time of classification, the computational complexity is bounded by few inner product calculations. Moreover, the implementation is shown to be very scalable. This makes it viable for deployment in applications where computational power and memory are limited, such as embedded environments. It enables the possibility for fast on-line optimisation using improved training samples. The approach is based on the concentration of measure effects and stochastic separation theorems and is illustrated with an example on the identification faulty processes in Computer Numerical Control (CNC) milling and with a case study on adaptive removal of false positives in an industrial video surveillance and analytics system.

Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.

To elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT. A total of 40 head CT datasets (normal, 16; haemorrhagic, 24) were evaluated by 15 physicians (5 board-certificated radiologists, 5 radiology residents, and 5 medical interns). The physicians attended 2 reading sessions without and with CAD. All physicians annotated the haemorrhagic regions with a degree of confidence, and the reading time was recorded in each case. Our CAD system was developed using 433 patients' head CT images (normal, 203; haemorrhagic, 230), and haemorrhage rates were displayed as corresponding probability heat maps using U-Net and a machine learning-based false-positive removal method. Sensitivity, specificity, accuracy, and figure of merit (FOM) were calculated based on the annotations and confidence levels. In patient-based evaluation, the mean accuracy of all physicians significantly increased from 83.7 to 89.7% (p < 0.001) after using CAD. Additionally, accuracies of board-certificated radiologists, radiology residents, and interns were 92.5, 82.5, and 76.0% without CAD and 97.5, 90.5, and 81.0% with CAD, respectively. The mean FOM of all physicians increased from 0.78 to 0.82 (p = 0.004) after using CAD. The reading time was significantly lower when CAD (43 s) was used than when it was not (68 s, p < 0.001) for all physicians. The CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage.

SSREnricher: a computational approach for large-scale identification of polymorphic microsatellites based on comparative transcriptome analysis.

Microsatellite (SSR) markers are the most popular markers for genetic analyses and molecular selective breeding in plants and animals. However, the currently available methods to develop SSRs are relatively time-consuming and expensive. One of the most factors is low frequency of polymorphic SSRs. In this study, we developed a software, SSREnricher, which composes of six core analysis procedures, including SSR mining, sequence clustering, sequence modification, enrichment containing polymorphic SSR sequences, false-positive removal and results output and multiple sequence alignment. After running of transcriptome sequences on this software, a mass of polymorphic SSRs can be identified. The validation experiments showed almost all markers (>90%) that were identified by the SSREnricher as putative polymorphic markers were indeed polymorphic. The frequency of polymorphic SSRs identified by SSREnricher was significantly higher (P < 0.05) than that of traditional and HTS approaches. The software package is publicly accessible on GitHub (https://github.com/byemaxx/SSREnricher).

A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images

To improve the early diagnosis and treatment of lung diseases automated lung segmentation from CT images is a crucial task for clinical decision. The segmentation of the lung region from the CT scans is a very challenging task due to the irregular shape and size of lungs, low contrast and fuzzy boundaries of the lung. The manual segmentation of lung CT images is a laborious task. Therefore, various approaches are suggested by the researcher in the recent past for the automated lung segmentation. However, the existing approaches either utilize low-level handcraft features or CNN based Fully Convolutional Networks. The low-level hand-craft feature-based approaches lead to poor generalization, while the shallower networks are unable to extract more discriminative features. Hence, in this study, we have implemented a deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation. Here, we have suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation. To take full advantage of the deeper network, we have utilized a set of schemes to ensure efficient training. First, we have implemented a U-Net architecture with residual block to overcome the problem of performance degradation. Further, various data augmentation techniques are utilized to improve the generalization capability of the proposed method. The experimental results show that the proposed method achieved competitive results over the existing methods with DSC of 98.63%, 99.62% and 98.68% for LUNA16, VESSEL12 and HUG-ILD dataset respectively.

Model-based unsupervised clustering for distinguishing Cuvier's and Gervais' beaked whales in acoustic data

Passive acoustic monitoring (PAM), particularly autonomous platforms, offers many advantages in monitoring phonating deep-diving marine mammals in oceanic environment. Relevant data can be obtained day and night continuously over long durations and in any weather conditions. It provides a cost-efficient solution with greater detection ranges when compared to traditional large research vessel and aerial visual surveys requiring keeping expert observers on station for long periods of time and relying on good visibility and calm seas. Therefore, PAM is becoming a preferred tool to assess population dynamics trends and health of deep-water marine mammal stocks. However the large volumes of collected data require robust automatic detection and classification algorithms to identify marine mammals in recordings. As for beaked whales, one of the challenging automatic processing goals is the identification of different species to advance our understanding of their role in the marine ecosystem. At present, traditional detection and classification methods employ searches for acoustic events above a user-defined signal-to-noise ratio threshold in the frequency band of interest and further rely on an experienced operator's manual inspection for species classification and removal of false positives. Current passive monitoring data collection systems yield large volumes of acoustic data, therefore a manual classification approach becomes very time-consuming and impractical. This paper focuses on developing a multi-stage automatic classifier for beaked whale species. The proposed method utilizes unsupervised machine clustering of signal attributes extracted from potential detection events flagged by an energy-band detector. The proposed algorithm was benchmarked against a manually annotated workshop dataset and applied to acoustic data collected in the northern Gulf of Mexico. The algorithm classifies beaked whale species in automatic mode with minimal operator involvement only at the validation stage. When compared with the manually annotated classification dataset, the proposed method achieved a recall rate of 82.8% for Cuvier's and 77.9% for Gervais' species in automatic mode. New insights on habitat use by different species of beaked whales in the Gulf of Mexico were gained when using the species-specific classifier. The high spatial resolution acoustic monitoring results showed that the habitat preferences of two dominating beaked whale species in the Gulf of Mexico support the habitat division (ecological niche) hypothesis.

A Novel Probabilistic Nuclei Segmentation Algorithm for H&amp;E Stained Histopathological Tissue Images

In this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&amp;amp;E stained histopathological tissue images. The proposed algorithm doesn’t require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels’ color value in the images with RGB color, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. The other contributions of the study are false positive removal algorithm and automatically determination of nuclei cluster for K-means. In order to compare and evaluate the performance of the proposed algorithm in terms of time and cost efficiency, K-Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations are revealed that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K-Means and Expert is 5%.

Novel Copy Move Forgery Detection Based on Repeated Feature Extraction and Delaunay Triangulation

Nowadays new and creative methods of forging images are developed with the invention of sophisticated softwares like Adobe photoshop. Tools available in such softwares will make the forged image look real which cannot be even identified by a naked eye. In this paper, key point based approach of taking out features using Scale Invariant Feature Transform (SIFT) is used. The feature points thus extracted are then modeled to get a set of triangles using Delaunay Triangulation method. These triangles are matched using mean vertex descriptor and the removal of false positives is done using the method of Random Sample Consensus (RANSAC). Implementation show that the proposed approach outdoes the equivalent methods