François Chung, Ph.D.

Tag: academic journal article

COMPAG 2015 - Academic journal

COMPAG 2015 – Academic journal article

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Publication

Rafael Redondo, Gloria Bueno, François Chung, Rodrigo Nava, J. Víctor Marcos, Gabriel Cristóbal, Tomás Rodríguez, Amelia González-Porto, Cristina Pardo, Óscar Déniz, Boris Escalante-Ramírez; Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy; In: Computers and Electronics in Agriculture (COMPAG), 110, pp. 56–69, 2015.

Abstract

Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines, computer vision comes to alleviate tedious recognition tasks. In this paper, we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour-inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grain’s contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.

Keywords

  • apiculture
  • automatic classification
  • bright-field microscopy
  • feature extraction
  • Fisher discriminant analysis
  • image processing
  • morphology descriptors
  • pollen
  • statistical descriptors
  • texture descriptors

References

Publication

Published version (ScienceDirect)
Preprint version (PDF)
Bibliographic reference (BibTeX)

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APIFRESH (Inspiralia project)
Micron 2015 (academic journal article)

Micron 2015 - Academic journal

Micron 2015 – Academic journal article

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Publication

J. Víctor Marcos, Rodrigo Nava, Gabriel Cristóbal, Rafael Redondo, Boris Escalante-Ramírez, Gloria Bueno, Óscar Déniz, Amelia González-Porto, Cristina Pardo, François Chung, Tomás Rodríguez; Automated pollen identification using microscopic imaging and texture analysis; In: Micron, 68, pp. 36-46, 2015.

Abstract

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralick's gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fisher's discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques.

Keywords

  • discrete Tchebichef moments
  • gray-level co-occurrence matrix
  • local binary patterns
  • Log-Gabor filters
  • pollen identification
  • texture analysis

References

Publication

Published version (ScienceDirect)
Preprint version (PDF)
Bibliographic reference (BibTeX)

Related articles

APIFRESH (Inspiralia project)
COMPAG 2015 (academic journal article)

CVIU 2013 - Academic journal

CVIU 2013 – Academic journal article

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Publication

François Chung, Hervé Delingette; Regional appearance modeling based on the clustering of intensity profiles; In: Computer Vision and Image Understanding (CVIU), 117 (6), pp. 705-717, 2013.

Abstract

Model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted Expectation-Maximization (EM) classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal PCA approach while relying on fewer profile modes.

Keywords

  • appearance modeling
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

References

Publication

Published version (ScienceDirect)
Preprint version (PDF)
Bibliographic reference (BibTeX)

Related articles

3D Anatomical Human (INRIA project)
Ph.D. Thesis 2011 (doctoral thesis)

TVC 2011 - Academic journal

TVC 2011 – Academic journal article

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Publication

François Chung, Jérôme Schmid, Nadia Magnenat-Thalmann, Hervé Delingette; Comparison of statistical models performance in case of segmentation using a small amount of training datasets; In: The Visual Computer (TVC), 27 (2), pp. 141-151, 2011.

Abstract

Model-based image segmentation has been extensively used in medical imaging to learn both shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using Magnetic Resonance (MR) images when only a small number of datasets is available for training. For shape, both priors based on Principal Component Analysis (PCA) and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from Expectation-Maximization (EM) clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.

Keywords

  • clustering
  • model-based segmentation
  • principal component analysis
  • statistical models

References

Publication

Published version (Springer)
Published version (HAL)
Preprint version (PDF)
Bibliographic reference (BibTeX)

Related articles

3D Anatomical Human (INRIA project)
Ph.D. Thesis 2011 (doctoral thesis)

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