1,720,980 research outputs found
Retinal image segmentation and quantification of vessel width in non-standard retinal datasets
Full text linkThe human retina has the potential to reveal important information about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. Automatic quantification of retinal vessel morphology and width is considered as a first step in computer assisted medical applications related to diagnosis and treatment planning. This work aims to quantify the blood vessels in noisy and pathological retinal images of school children with uneven illumination and containing complex vessel profiles. In this thesis, we have presented two methodologies of retinal vessel segmentation and an algorithm for vessel width measurement. The unsupervised method of retinal segmentation is based on detection of vessel centrelines and followed by computing the vessel shape and the orientation map using morphological bitplane slicing. A supervised method for segmentation of blood vessels by using an ensemble classifier of boosted and bagged decision trees is also presented. The feature vector encodes information to successfully handle both normal and pathological retinas with bright and dark lesions simultaneously. The obtained performance metrics illustrate that this method outperforms most of the state-of-the-art methodologies of retinal vessel segmentation. The method is computationally fast in training and classification and needs fewer samples for training than other supervised methods. It is training set robust as it offers a better performance even when it is trained and tested on different sets of retinal images. A new public database of the retinal images taken from multi-ethnic school children is presented along with the ground truths of vessel segmentation and width measurement. We have also introduced a robust and accurate methodology for measuring the calibre of vessel segments in retinal images of multi-ethnic children. The vessel centrelines are detected from the vessel probability map image resulting from ensemble classification. The vessel branch points and crossovers are identified and removed from the vessel centreline image to obtain vessel segments followed by computing the local vessel orientation of the vessel segments. The width of each vessel segment is estimated using a two dimensional model with incorporated Gaussian (for ordinary vessels) as well as Difference of Gaussian profiles (for vessels with a central reflex). The automated methods for quantification of retinal vessel morphology and width may be used as an alternative to the time consuming subjective clinical evaluation for monitoring the progression of retinopathies and their association with normal and abnormal vascular patterns. This may enable a quick diagnosis, treatment availability, prognosis, and facilitation of clinical heath-care procedures in remote areas
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
Full text linkMedical image segmentation assists in computer-aided diagnosis, surgeries,
and treatment. Digitize tissue slide images are used to analyze and segment
glands, nuclei, and other biomarkers which are further used in computer-aided
medical applications. To this end, many researchers developed different neural
networks to perform segmentation on histological images, mostly these networks
are based on encoder-decoder architecture and also utilize complex attention
modules or transformers. However, these networks are less accurate to capture
relevant local and global features with accurate boundary detection at multiple
scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention
Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE)
Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our
proposed network on two publicly available datasets for medical image
segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with
1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS
dataset. Implementation Code is available at this link: https://bit.ly/HistoSegComment: Accepted by 2022 12th International Conference on Pattern Recognition
Systems (ICPRS), For Implementation Code see https://bit.ly/HistoSe
Computer vision algorithms applied to retinal vessel segmentation and quantification of vessel caliber
No full textAFINITI: attention-aware feature integration for nuclei instance segmentation and type identification
No full textAccurately identifying and analyzing nuclei is pivotal for both the diagnosis and examination of cancer. However, the complexity of this task arises due to the presence of overlapping and cluttered nuclei with blurred boundaries, variations in nuclei sizes and shapes, and an imbalance in the available datasets. Although current methods utilize region proposal techniques and feature encoding frameworks, but they often fail to precisely identify occluded nuclei instances. We propose a model named AFINITI, which is both simple, efficient, achieves high accuracy, recognizes instance boundaries cluttered and overlapping nuclei, and addresses class imbalance issues. Our approach utilizes nuclei pixel positional information and a novel loss function to yield accurate class information for each nuclei. Our network features a lightweight, attention-aware feature fusion architecture with separate instance probability, shape radial estimator, and classification heads. We use a compound classification loss function to assign a weighted loss to each class according to its occurrence frequency, thereby addressing the class imbalance issues. The AFINITI model outperforms current leading networks across eight major publicly available nuclei segmentation datasets achieving up to an 8% increase in Dice Similarity Coefficient (DSc) and a 17% increase in Panoptic Quality (PQ) compared to existing techniques demonstrating its effectiveness and potential for clinical applications. The source code and the weights of the trained model have been released to the public and can be accessed at: https://github.com/Vision-At-SEECS/AF-Net. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Unlocking plant secrets: A systematic review of 3D imaging in plant phenotyping techniques
No full textPhenotyping is a systematic process of quantifying and assessing a wide range of structural and physiological traits to understand the intricate interplay between an organism's genetic makeup, its surrounding environment, and management practices, often referred to as genome-to-environment (GxE) interaction. In the context of plants, these traits can include aspects such as plant height, stem diameter, leaf size, angle, and shape, chlorophyll content, biomass, leaf area, etc. 3D plant phenotyping plays a crucial role in advancing our understanding of plant biology, improving crop breeding, and agricultural practices. 3D imaging has become a powerful phenotyping tool, offering in-depth insights into plant structures and traits. In contrast to 2D imaging, 3D imaging enables precise measurement of plant traits that cannot be sufficiently evaluated in two dimensions by overcoming challenges such as partial occlusion through the utilization of depth perception and multiple viewpoints. However, even with significant recent progress, various challenges persist, including the need for well-designed experimental setups for standardized data collection, the automation of processing pipelines, and the robust analysis techniques of 3D representations, which still impede the widespread adoption of 3D plant phenotyping. To propel the progress of 3D imaging-based phenotyping, an all-encompassing assessment of existing strategies is imperative, yet there is currently a lack of specialized reviews that scrutinize and emphasize distinct facets for future enhancement. To bridge this gap, we perform a systematic survey of 81 research studies that employ 3D imaging for various trait assessments of plants. Our review thoroughly investigates the stages of data acquisition, encompassing sensing technologies, representations, preprocessing approaches, analysis methodologies, and techniques for estimating phenotypic traits. We believe that this comprehensive review will serve as a valuable guide for researchers and professionals engaged in high throughput plant phenotyping, equipping them to formulate effective experimental setups and utilize appropriate processing and analysis methods, thereby fostering its continued advancement. © 2024 Elsevier B.V
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