Determining the subcellular location of new proteins from microscope images using local features

Coelho, Luís Pedro; Kangas, Joshua; Naik, Armaghan W.; Osuna-Highley, Elvira; Glory-Afshar, Estelle; Fuhrman, Margaret H.; Simha, Ramanuja; Berget, Peter B.; Jarvik, Jonathan W.; Murphy, Robert F.

doi:10.1093/bioinformatics/btt392

Cited by 64 publications

(59 citation statements)

References 32 publications

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“…Our classification accuracy is slightly lower that those reported in [5], [6] and [7], but we have not yet tried to optimize the different parameters in our implementations. Our results are slightly higher than those reported in [4]. …”

Section: Resultscontrasting

confidence: 91%

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Exploring Local Features and the Bag-of-Visual-Words Approach for Bioimage Classification

Godil

Lian

Wagan

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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With recent advances in imaging technologies large numbers of bioimages are currently being acquired. Automated classification of these bio-images is a very important and challenging problem.Here we investigate the capabilities of local features and the Bagof-Visual-Words (BOV) approach in the area of bioimage classification. We have tested both sparse and dense placement of local features. The local feature that we have tested is ScaleInvariant Feature Transform (SIFT), but we are in the process of testing other local features. The standard BOV approach is based on counting the number of local descriptors assigned to each quantization. In our case we are also using other statistics (mean and covariance of local descriptors). The classifier used for this study is the Support Vector Machine (SVM). We have performed classification experimentation on the well-tested single cell dataset of 2D HeLa from CMU and have achieved performance similar to the state of the art.

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Section: Resultscontrasting

confidence: 91%

“…Besides our study, which is based on local features and BOV approach, Coelho et al [4] is the only other study based on it. In spite of its simplicity, flexibility, and effectiveness, the BOV method, which originated from the text retrieval field, has not been used in the area of bioimage classification.…”

Section: Introductionmentioning

confidence: 98%

Exploring Local Features and the Bag-of-Visual-Words Approach for Bioimage Classification

Godil

Lian

Wagan

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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“…This section presents an edited version of the code used in a previously published study of computer vision techniques for bioimage analysis [10]. 2 The code was edited to remove superfluous details, however the overall logic is preserved as the original version was already based on Jug.…”

Section: Examplementioning

confidence: 99%

“…It has been used by the author in several projects [10,11,12,57]. Others have used the framework in other contexts, such as physics [2], machine learning [30,32,52], metereology [60,61], and it is used in the pyfssa package for algorithmic finite-size scaling analysis [55].…”

Section: Reuse Potentialmentioning

confidence: 99%

Jug: Software for Parallel Reproducible Computation in Python

Coelho¹

2017

JORS

Self Cite

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“…With these extracted descriptors, supervised classification models such as support vector machine (SVM) [12, 13, 18, 19, 22–24, 27, 29, 31], subspace learning [10, 11, 14–16, 26], multiple instance learning [17, 25] and sparse representation [21, 32] are applied. However, the classification performance is often largely affected by the small number of training data available for bioimage research.…”

Section: Introductionmentioning

confidence: 99%

Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

et al. 2016

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BackgroundBioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.ResultsWe evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis.ConclusionsWe present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.

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Determining the subcellular location of new proteins from microscope images using local features

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 64 publications

References 32 publications

Exploring Local Features and the Bag-of-Visual-Words Approach for Bioimage Classification

Exploring Local Features and the Bag-of-Visual-Words Approach for Bioimage Classification

Jug: Software for Parallel Reproducible Computation in Python

Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

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