Automatic Acquisition of Immunofluorescence Images: Algorithms and Evaluation

Soda, Paolo; Rigon, Amelia; Afeltra, Antonella; Iannello, Giulio

doi:10.1109/cbms.2006.53

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…Different workflow aspects of the sample evaluation have been explored over the years. Researchers have analysed the image acquisition [11, 12], the effects of image preprocessing on sample recognition [13, 14], and methods for precise cell image segmentation [15–17]. The main focus, however, remains on recognition of staining patterns, where several recently presented methods have outperformed human experts [13, 18].…”

Section: Related Workmentioning

confidence: 99%

Generalised deep learning framework for HEp‐2 cell recognition using local binary pattern maps

et al. 2020

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The authors propose a novel HEp‐2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state‐of‐the‐art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, αLBP‐maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.

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Section: Related Workmentioning

confidence: 99%

Generalised deep learning framework for HEp‐2 cell recognition using local binary pattern maps

et al. 2020

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“…In [5] Hiemann et al proposed a method for quality evaluation of fluorescence images based on o set of shape and textural parameters extracted from the images. Soda et al in [15] propose an autofocus function that can deal with photobleaching effect during acquisition. Segmentation of fluorescence cells in indirect immunofluorescence images (IIF) was performed by Yu-Len et al in [18] using the similarity-based watershed algorithm as a marker to prevent over-segmentation, and in [19] using an adaptive edged-based segmentation method.…”

Section: Introductionmentioning

confidence: 99%

HEp-2 Cells classification via fusion of morphological and textural features

Theodorakopoulos

Kastaniotis

Economou

et al. 2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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Autoimmune diseases are proven to be connected with the occurrence of autoantibodies in patient serum. Antinuclear autoantibodies (ANAs) identification can be accomplished in a laboratory using indirect immunofluorescence (IIF) imaging. ANAs are characterized by specific "visual" patterns on a humane epithelial cell line (HEp-2). The identification stage is usually done by trained and highly qualified physicians through visual inspection of slides using a fluorescence microscope. The presence of subjectivity in the identification process, the interobserver variability, the increasing demand of highly specialized personnel, suggest that a realization of an automatic classification system is of great significance for the field of autoimmune diseases diagnosis. Moreover CAD systems can be used in a collaborative scheme in order to augment the physicians' capabilities. In this paper a system for automatic classification of staining patterns on single-cell fluorescence images is proposed. Our method utilizes morphological features extracted from a set of binary images derived via multi-level thresholding of fluorescence images. Furthermore, a modified version of Uniform Local Binary Patterns descriptor is incorporated in order to capture local textural information. The classification is performed using a non-linear SVM Classifier. The proposed method is evaluated using a publicly available dataset, recently released for the purposes of HEP-2 Cells classification competition at ICPR 2012, achieving up to 95.9% overall classification accuracy.

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“…To implement automatic focusing, a focus measure, or as called in this paper, an evaluation function, for determining the image definition is necessary. Various auto-focusing algorithms have been proposed and used in applications, such as in [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A New Focus Measure Using Block Maxima of Image Gradients

Guo

Bao

2009

2009 Third International Symposium on Intelligent Information Technology Application

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Auto-focusing is an important problem for various imaging systems. A good focus measure, or evaluation function, is critical for the accomplishment of this task. Some evaluation functions are introduced. Analysis is done on evaluation functions using global data such as the squared image gradient magnitude of the whole image, asserting that these approaches may suffer from the indiscriminate use of the data. A new evaluation function is proposed. By partitioning the image into small blocks and using the maximal squared gradient magnitude in each block to construct the evaluation value, impacts of strong off-boundary gradients deteriorating the performance of the "global" approaches can be suppressed. Experiments on a real-world microscopy video data set show that our method exhibits higher focusing accuracy.

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Automatic Acquisition of Immunofluorescence Images: Algorithms and Evaluation

Cited by 20 publications

References 12 publications

Generalised deep learning framework for HEp‐2 cell recognition using local binary pattern maps

Generalised deep learning framework for HEp‐2 cell recognition using local binary pattern maps

HEp-2 Cells classification via fusion of morphological and textural features

A New Focus Measure Using Block Maxima of Image Gradients

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