Wndchrm – an open source utility for biological image analysis

Shamir, Lior; Orlov, Nikita; Eckley, D. Mark; Macura, Tomasz; Johnston, Josiah; Goldberg, Ilya G.

doi:10.1186/1751-0473-3-13

Cited by 205 publications

(328 citation statements)

References 20 publications

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“…The galaxies used in the experiment were galaxies classified as spiral galaxies in the catalogue of broad morphology of ∼3,000,000 SDSS Data Release 8 galaxies (Kuminski & Shamir 2016), which was generated automatically by applying the Wndchrm image classifier (Shamir et al 2008(Shamir et al , 2013 to the galaxy images (Shamir 2009;Kuminski et al 2014). The initial set of ∼3,000,000 galaxies was selected such that all galaxies had a Petrosian radius (measured on the r band) of at least 5.5", the Petrosian radius error was less than 5", and the flags were selected such that none of the objects was identified as "bad sky", "bad radial", "too large", too close the the edge of the frame, or had more than one peak or Petrosian radius (Kuminski & Shamir 2016).…”

Section: Datamentioning

confidence: 99%

Photometric Asymmetry Between Clockwise and Counterclockwise Spiral Galaxies in SDSS

Shamir

2017

Publ. Astron. Soc. Aust.

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While galaxies with clockwise and counterclockwise handedness are visually different, they are expected to be symmetric in all of their other characteristics. Previous experiments using both manual analysis and machine vision have shown that the handedness of Sloan Digital Sky Survey (SDSS) galaxies can be predicted with accuracy significantly higher than mere chance using its photometric data alone, showing that the photometric pipeline of SDSS is sensitive to the handedness of the galaxy. However, some of these previous experiments were based on manually classified galaxies, and the results may therefore be subjected to bias originated from the human perception. This paper describes an experiment based on a set of 162,514 celestial objects classified as clockwise and counterclockwise spiral galaxies in a fully automatic process, showing that the source of the asymmetry in SDSS database is not the human perception bias. The results are compared to two smaller datasets, and confirm the observation that the handedness of galaxies in SDSS database can be predicted by the way SDSS measures their photometry, and show that the position angle of counterclockwise galaxies computed by SDSS photometry pipeline is consistently higher than the position angle computed for galaxies with clockwise patterns. The experiment also shows statistically significant differences in the measured magnitude of SDSS galaxies, according which galaxies with clockwise patterns imaged by SDSS are brighter than galaxies with counterclockwise patterns. The magnitude of that difference changes across RA ranges, and exhibits a strong correlation with the cosine of the right ascension.

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Section: Datamentioning

confidence: 99%

Photometric Asymmetry Between Clockwise and Counterclockwise Spiral Galaxies in SDSS

Shamir

2017

Publ. Astron. Soc. Aust.

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“…Moreover, such a tool has been used in medical image registrations. Thus, its application to biology has garnered considerable attention in biology [58,14,50].…”

Section: Organellar Classifications Via Svm With Hlacmentioning

confidence: 99%

Communication Platform for Image Analysis and Sharing in Biology

Murakami

Tawara

Nishimura

et al. 2014

IJNC

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Image analysis is crucial to medical and biological applications. Recent advances in imaging technology have led to the demand for processing and visualizing a large amount of three-dimensional (3D) biomedical images. In addition, cloud computing has become popular for managing big data. Unfortunately, conventional image-processing systems either lack cloud computing services or advanced 3D processing abilities. In this paper, we present a novel cloud-based system for sharing, processing, and visualizing 3D biomedical images. Our system employs a standard web browser as a client interface that interactively communicates with high-performance servers. Thus, an inexpensive tablet PC without an advanced graphics processing unit (GPU) can be used for 3D image processing and visualization. Our system provides the sharing of limited software and hardware resources, and it allows for effective collaboration between researchers. We demonstrate the applicability and functionality of the system by examining typical case studies on biomedical images. We also examine the performance of our system numerically.

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“…Image transforms and paths of the compound transforms described in [14] The image features and transforms above have been implemented as part of an open source software [15] and we directly utilize it in our study. For details of the feature extraction algorithms, one may refer to [10] and the references therein.…”

Section: Feature Extractionmentioning

confidence: 99%

“…[4,9,16,18,19]). Most of the works developed features that are specific to the domains related to art and color theories, which lacks generality and makes it difficult for researchers who are unfamiliar with computer vision algorithms to perform image analysis on their own data [15]. Besides, the image datasets used in these works are mainly scenery and photographic images containing recognizable objects and faces which are likely to distort people's emotions.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with a designed baseline method, we can achieve significantly better results on classifying image emotions between exciting and boring, relaxing and irritating, with a relatively small number of image samples. The features used in this article have been implemented by Shamir et al [15] as an open source software and the abstract art image dataset that we collected has been made downloadable to the research community 1 .…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

Augilius

Honkela

et al. 2011

Advances in Intelligent Data Analysis X

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Abstract.In this work, we study people's emotions evoked by viewing abstract art images based on traditional low-level image features within a binary classification framework. Abstract art is used here instead of artistic or photographic images because those contain contextual information that influences the emotional assessment in a highly individual manner. Whether an image of a cat or a mountain elicits a negative or positive response is subjective. After discussing challenges concerning image emotional semantics research, we empirically demonstrate that the emotions triggered by viewing abstract art images can be predicted with reasonable accuracy by machine using a variety of low-level image descriptors such as color, shape, and texture. The abstract art dataset that we created for this work has been made downloadable to the public.

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Wndchrm – an open source utility for biological image analysis

Abstract: We suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.

Cited by 205 publications

References 20 publications

Photometric Asymmetry Between Clockwise and Counterclockwise Spiral Galaxies in SDSS

Photometric Asymmetry Between Clockwise and Counterclockwise Spiral Galaxies in SDSS

Communication Platform for Image Analysis and Sharing in Biology

Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

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