Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers

Yin, Zhaozheng; Bise, Ryoma; Chen, Mei; Kanade, Takeo

doi:10.1109/isbi.2010.5490399

Cited by 56 publications

(34 citation statements)

References 11 publications

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“…Wang (2010) considers a Markov Random Field in a Bayesian formulation to segment cancerous structures by classifying individual pixels in H&E stained images. Yin et al (2010) uses a bag of local Bayesian classifiers to determine which pixels in the image belong to cells. Zampirolli et al (2010) starts by obtaining geometrical structures from the tissue using morphological techniques.…”

Section: Related Workmentioning

confidence: 99%

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Janssens

Antanas

Derde

et al. 2013

Medical Image Analysis

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Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until now this has not been the case when applied to skeletal muscle histology images. We introduce Charisma, a new top-down cell segmentation framework for histology images which combines image processing techniques, a supervised trained classifier and a novel robust clump splitting algorithm. We evaluate our framework on real-world data from intensive care unit patients. Considering both segmentation and cell property distributions, the results obtained by our method correspond well to the ground truth, outperforming other examined methods.

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Section: Related Workmentioning

confidence: 99%

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Janssens

Antanas

Derde

et al. 2013

Medical Image Analysis

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“…Other soft-segmentation methods such as a bag of local Bayesian classification [8] can be adopted here to provide an initialization for our iterative restoration algorithm. …”

Section: Initialization Inferred From a Look-up Tablementioning

confidence: 99%

Restoring artifact-free microscopy image sequences

Yin

Kanade

2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Phase contrast and differential interference contrast, which are used to capture microscopy images of living cells, contain a few artifacts such as halo and shadow-cast effect. Removing these artifacts from microscopy images facilitates automated microscopy image analysis, such as the cell segmentation that is a critical step in cell tracking systems. We propose a restoration algorithm based on the microscopy imaging model and consider temporal consistency when restoring time-lapse microscopy image sequences. The artifact-free microscopy images are restored by minimizing a regularized quadratic cost function that is adaptable to input image properties. Our method achieves high segmentation accuracy and low computational cost compared to the previous methods.

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“…Automated detectors are increasingly used in biomedical imaging applications [1,2,3]. These algorithms solve visual pattern recognition problems, and are often based on a statistical classifier which is learned from a training set.…”

Section: Introductionmentioning

confidence: 99%

A comparison of algorithms and humans for mitosis detection

Giusti

Caccia

Cireşan

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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We consider the problem of detecting mitotic figures in breast cancer histology slides. We investigate whether the performance of stateof-the-art detection algorithms is comparable to the performance of humans, when they are compared under fair conditions: our test subjects were not previously exposed to the task, and were required to learn their own classification criteria solely by studying the same training set available to algorithms. We designed and implemented a standardized web-based test based on the publicly-available MITOS dataset, and compared results with the performance of the 6 topscoring algorithms in the ICPR 2012 Mitosis Detection Contest. The problem is presented as a classification task on a balanced dataset. 45 different test subjects produced a total of 3009 classifications. The best individual (accuracy = 0.859 ± 0.012), is outperformed by the most accurate algorithm (accuracy = 0.873 ± 0.004). This suggests that state-of-the-art detection algorithms are likely limited by the size of the training set, rather than by lack of generalization ability.

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Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers

Cited by 56 publications

References 11 publications

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Restoring artifact-free microscopy image sequences

A comparison of algorithms and humans for mitosis detection

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