informИнформатикаInformatics1816-03012617-6963UIIP NASBinform-123Research ArticleОБРАБОТКА СИГНАЛОВ, ИЗОБРАЖЕНИЙ, РЕЧИ, ТЕКСТА И РАСПОЗНАВАНИЕ ОБРАЗОВSIGNAL, IMAGE, SPEECH, TEXT PROCESSING AND PATTERN RECOGNITIONМЕТОДИКА КОЛИЧЕСТВЕННОГО ОПИСАНИЯ БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ СЛОВАРЕЙ СУПЕРПИКСЕЛОВA METHOD FOR QUANTITATIVE DESCRIPTION OF BIOMEDICAL IMAGES BASED ON SUPERPIXEL DICTIONARIESЛевчукВ. А.LiauchukV. A.vitali.liauchuk@gmail.comКовалевВ. А.KovalevV. A.Объединенный институт проблем информатики НАН БеларусиBelarus201602102016014957Copyright © Левчук В.А., Ковалев В.А., 20162016Левчук В.А., Ковалев В.А.Liauchuk V.A., Kovalev V.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/123

Предлагается методика количественного описания биомедицинских изображений, основанная на разбиении целевого изображения на суперпикселы и их сопоставлении с ранее подготовленным словарем суперпикселов, характерных для изображений анализируемого типа. Методика протестирована на задачах распознавания биомедицинских изображений трех типов (КТ-снимков легкого, гистологических изображений образцов тканей яичников и тканей щитовидной железы). Экспериментально показывается, что предлагаемая методика обеспечивает результаты, сравнимые по качеству распознавания с традиционными методами описания структуры изображений либо превосходящие их.

With this study, a method for quantitative description of biomedical images based on splitting the target image into superpixels followed by categorization using precalculated superpixel dictionaries is proposed. The method has been tested on the tasks of recognition of biomedical images of three types: lung CT images, histology images of ovary and thyroid tissues. The results of the experiments performed suggest that the method proposed may provide recognition performance comparable or better than when using conventional methods of texture description.

ReferencesKovalev, V. Color Co-occurence Descriptors for Querying-by-Example / V. Kovalev, S. Volmer // Proc. of the 1998 Conf. on MultiMedia Modeling. – Switzerland, 1998. – P. 32–38.Kovalev, V. Color Co-occurence Descriptors for Querying-by-Example / V. Kovalev, S. Volmer // Proc. of the 1998 Conf. on MultiMedia Modeling. – Switzerland, 1998. – P. 32–38.Content-based image retrieval as a method for melanoma diagnosis / V. Liauchuk [et al.] // Proc. of the 29th Intern. Congress on Computer Assisted Radiology and Surgery (CARS– 2015). – Spain, 2015. – Vol. 10. – P. 291–292.Content-based image retrieval as a method for melanoma diagnosis / V. Liauchuk [et al.] // Proc. of the 29th Intern. Congress on Computer Assisted Radiology and Surgery (CARS– 2015). – Spain, 2015. – Vol. 10. – P. 291–292.Pinto, N. A high-throughput screening approach to discovering good forms of biologically inspired visual representation / N. Pinto, D. Doukhan, D. Cox // PLoS Computational Biology. – 2009. – Vol. 5(11). – P. 1–12.Pinto, N. A high-throughput screening approach to discovering good forms of biologically inspired visual representation / N. Pinto, D. Doukhan, D. Cox // PLoS Computational Biology. – 2009. – Vol. 5(11). – P. 1–12.Evaluating Bag-of-Visual-Words Representations in Scene Classification / J. Yang [et al.] // Proc. of the Intern. Workshop on Multimedia Information Retrieval. – Germany, Augsburg, 2007. – P. 197–206.Evaluating Bag-of-Visual-Words Representations in Scene Classification / J. Yang [et al.] // Proc. of the Intern. Workshop on Multimedia Information Retrieval. – Germany, Augsburg, 2007. – P. 197–206.Quantized Image Patches Co-occurrence Matrix: A New Statistical Approach for Texture Classification using Image Patch Exemplars / Zh. Liu [et al.] // Proc. of the 3th Intern. Conf. on Digital Image Processing (ICDIP 2011). – China, Chengdu, 2011. – Vol. 8009. – P. 56–64.Quantized Image Patches Co-occurrence Matrix: A New Statistical Approach for Texture Classification using Image Patch Exemplars / Zh. Liu [et al.] // Proc. of the 3th Intern. Conf. on Digital Image Processing (ICDIP 2011). – China, Chengdu, 2011. – Vol. 8009. – P. 56–64.Fulkerson, B. Class segmentation and object localization with superpixel neighborhoods / B. Fulkerson, A. Vedaldi, S. Soatto // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Japan. – 2009. – P. 8.Fulkerson, B. Class segmentation and object localization with superpixel neighborhoods / B. Fulkerson, A. Vedaldi, S. Soatto // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Japan. – 2009. – P. 8.Levinshtein, A. Multiscale symmetric part detection and grouping / A. Levinshtein, C. Sminchisescu, S. Dickinson // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Sapan. – 2009. – Vol. 104. – P. 117–134.Levinshtein, A. Multiscale symmetric part detection and grouping / A. Levinshtein, C. Sminchisescu, S. Dickinson // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Sapan. – 2009. – Vol. 104. – P. 117–134.Li, X. An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding / X. Li, Y. Guo // Proc. of the British Machine Vision Conference. – Surrey, 2012. – P. 1–11.Li, X. An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding / X. Li, Y. Guo // Proc. of the British Machine Vision Conference. – Surrey, 2012. – P. 1–11.Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation / Q. Gao [et al.] // SPIE 9034, Medical Imaging 2014: Image Proc. – 2014. – Vol. 9034. – Р. 903407–903407-6.Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation / Q. Gao [et al.] // SPIE 9034, Medical Imaging 2014: Image Proc. – 2014. – Vol. 9034. – Р. 903407–903407-6.Micusik, B. Semantic segmentation of street scenes by superpixel co-occurrence and 3D geometry / B. Micusik, J. Kosecka // IEEE Workshop on Video-Oriented Object and Event Classification (VOEC). – Japan, 2009. – P. 625–632.Micusik, B. Semantic segmentation of street scenes by superpixel co-occurrence and 3D geometry / B. Micusik, J. Kosecka // IEEE Workshop on Video-Oriented Object and Event Classification (VOEC). – Japan, 2009. – P. 625–632.He, X. Learning and incorporating top-down cues in image segmentation / X. He, R. Zemel, D. Ray // Proc. of the 9th European Conf. on Computer Vision. – Austria, 2006. – P. 338–351.He, X. Learning and incorporating top-down cues in image segmentation / X. He, R. Zemel, D. Ray // Proc. of the 9th European Conf. on Computer Vision. – Austria, 2006. – P. 338–351.Sicre, R. SuperPixel based angular differences as a mid-level image descriptor / R. Sicre, E. Tasli, T. Gevers // Proc. of the 22th Intern. Conf. on Pattern Recognition (ICPR). – Sweden, 2014. – P. 3732–3737.Sicre, R. SuperPixel based angular differences as a mid-level image descriptor / R. Sicre, E. Tasli, T. Gevers // Proc. of the 22th Intern. Conf. on Pattern Recognition (ICPR). – Sweden, 2014. – P. 3732–3737.Content-based image retrieval of histology images / A. Dmitruk [et al.] // Proc. of Swedish Symposium on Image Analysis (SSBA-2010). – Uppsala, Sweden, 2010. – P. 91–92.Content-based image retrieval of histology images / A. Dmitruk [et al.] // Proc. of Swedish Symposium on Image Analysis (SSBA-2010). – Uppsala, Sweden, 2010. – P. 91–92.Tighe, J. SuperParsing: Scalable nonparametric image parsing with superpixels / J. Tighe, S. Lazebnik // ECCV’10 Proc. of the 11th European Conf. on Computer Vision. – Germany, Heidelberg, 2010. – P. 352–365.Tighe, J. SuperParsing: Scalable nonparametric image parsing with superpixels / J. Tighe, S. Lazebnik // ECCV’10 Proc. of the 11th European Conf. on Computer Vision. – Germany, Heidelberg, 2010. – P. 352–365.SLIC Superpixels Compared to State-of-the-art Superpixel Methods / R. Achanta [et al.] // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2012. – Vol. 34(11). – P. 2274– 2282.SLIC Superpixels Compared to State-of-the-art Superpixel Methods / R. Achanta [et al.] // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2012. – Vol. 34(11). – P. 2274– 2282.Ковалев, В.А. Влияние мер близости в пространстве признаков на качество поиска медицинских изображений по содержанию / В.А. Ковалев, А.А. Дмитрук // Информатика. – 2011. – № 30. – С. 5–11.Ковалев, В.А. Влияние мер близости в пространстве признаков на качество поиска медицинских изображений по содержанию / В.А. Ковалев, А.А. Дмитрук // Информатика. – 2011. – № 30. – С. 5–11.Efron, B. Bootstrap Methods: Another Look at the Jackknife / B. Efron // Annals of Statistics. – 1979. – Vol. 1. – P. 1–26.Efron, B. Bootstrap Methods: Another Look at the Jackknife / B. Efron // Annals of Statistics. – 1979. – Vol. 1. – P. 1–26.Matas, T. Extended set of local binary patterns for rapid object detection / T. Matas, J. Matas // Proc. of the Computer Vision Winter Workshop. – Czech Republic, Nové Hrady, 2010. – Vol. 2010. – P. 37–43.Matas, T. Extended set of local binary patterns for rapid object detection / T. Matas, J. Matas // Proc. of the Computer Vision Winter Workshop. – Czech Republic, Nové Hrady, 2010. – Vol. 2010. – P. 37–43.

The authors declare that there are no conflicts of interest present.