radioelectronicsИзвестия высших учебных заведений России. РадиоэлектроникаJournal of the Russian Universities. Radioelectronics1993-89852658-4794Saint Petersburg Electrotechnical University10.32603/1993-8985-2022-25-3-96-117radioelectronics-641Research ArticleПРИБОРЫ МЕДИЦИНСКОГО НАЗНАЧЕНИЯ, КОНТРОЛЯ СРЕДЫ, ВЕЩЕСТВ, МАТЕРИАЛОВ И ИЗДЕЛИЙMEDICAL DEVICES, ENVIRONMENT, SUBSTANCES, MATERIAL AND PRODUCTПрименение модели внешнего вида текстуры для сегментации легочных узлов при компьютерной томографии грудной клеткиApplication of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chesthttps://orcid.org/0000-0002-7060-8826ШариатиФ.ShariatyF.

Шариати Фаридоддин - магистр (2021), ассистент (2021) Высшей школы прикладной физики и космических технологий.

ул. Политехническая, д. 29, Санкт-Петербург, 195251.

Faridoddin Shariati - Master in "Electrical engineering" (2021), Assistant (2021) of the Higher School of Applied Physics and Space Technologies of Peter the Great St. Petersburg Polytechnic University.

29 Politekhnicheskaya St., St Petersburg 195251.

shariati2.f@edu.spbstu.ruhttps://orcid.org/0000-0003-0726-6613ПавловВ. А.PavlovV. A.

Павлов Виталий Александрович - кандидат технических наук (2020), ассистент (2021) Высшей школы прикладной физики и космических технологий СПбПУ Петра Великого; научный сотрудник Центра персонализированной медицины НМИЦ им. В.А. Алмазова.

ул. Политехническая, д. 29, Санкт-Петербург, 195251.

Vitalii A. Pavlov - Cand. Sci. (Eng.) (2020), Assistant (2021) of the Higher School of Applied Physics and Space Technologies of Peter the Great St. Petersburg Polytechnic University; Researcher of Center for Personalized Medicine of Almazov National Medical Research Centre.

29 Politekhnicheskaya St., St Petersburg 195251.

pavlov_va@spbstu.ruhttps://orcid.org/0000-0003-3398-3616ЗавьяловС. В.ZavjalovS. V.

Завьялов Сергей Викторович - кандидат технических наук (2015), доцент (2020) Высшей школы прикладной физики и космических технологий.

ул. Политехническая, д. 29, Санкт-Петербург, 195251.

Sergey V. Zavyalov - Cand. Sci. (Eng.) (2015), Associate Professor (2020) of the Higher School of Applied Physics and Space Technologies of Peter the Great St. Petersburg Polytechnic University.

29 Politekhnicheskaya St., St Petersburg 195251.

zavyalov_sv@spbstu.ruhttps://orcid.org/0000-0003-1129-0667ОруджиМ.OroojiM.

Оруджи Махди - Ph.D. (2012) в области электротехники по специальности "Системы связи", Университет штата Луизиана; приглашенный профессор Калифорнийского университета.

1 Shields Ave, Дэвис, Калифорния 95616.

Mahdi Orooji - Ph.D. (2012) in electrical engineering with a degree in communications systems from Louisiana State University; Visiting Professor at the University of California.

1 Shields Ave, Davis, CA 95616.

morooji@gmail.comhttps://orcid.org/0000-0001-9948-7303ПервунинаТ. М.PervuninaT. M.

Первунина Татьяна Михайловна - доктор медицинских наук (2019), доцент (2015).

ул. Аккуратова, д. 2, Санкт-Петербург, 197341.

Tatyana M. Pervunina - Dr Sci. (Medicine) (2019), Associate Professor (2015) of Almazov National Medical Research Centre.

2 Akkuratova St., St Petersburg 197341.

ptm.pervunina@yandex.ruСанкт-Петербургский политехнический университет Петра ВеликогоРоссияPeter the Great St. Petersburg Polytechnic UniversityRussian FederationСанкт-Петербургский политехнический университет Петра Великого; Национальный медицинский Исследовательский центр имени В.А. АлмазоваРоссияPeter the Great St. Petersburg Polytechnic University; Almazov National Medical Research CentreRussian FederationКалифорнийский университетСоединённые Штаты АмерикиUniversity of CaliforniaUnited StatesНациональный медицинский Исследовательский центр имени В.А. АлмазоваРоссияAlmazov National Medical Research CentreRussian Federation20222806202225396117Copyright © Шариати Ф., Павлов В.А., Завьялов С.В., Оруджи М., Первунина Т.М., 20222022Шариати Ф., Павлов В.А., Завьялов С.В., Оруджи М., Первунина Т.М.Shariaty F., Pavlov V.A., Zavjalov S.V., Orooji M., Pervunina T.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://re.eltech.ru/jour/article/view/641Введение

Введение. Рак легких ежегодно становится причиной более миллиона смертей во всем мире. Компьютерная диагностика (Computer-Aided Detection - CAD) является очень важным инструментом для идентификации поражений легких. В целом технологическую линию системы CAD можно разделить на четыре основных этапа: предварительную обработку, локализацию, извлечение признаков и классификацию. Поскольку для локализации при обработке медицинских изображений требуется сегментация, этот этап стал важной и сложной проблемой и было проведено много исследований новых методов сегментации.

Цель работы

Цель работы. Применение модели внешнего вида текстуры для сегментации легочных узлов при компьютерной томографии грудной клетки.

Материалы и методы

Материалы и методы. Предложена модель текстурного представления, которая является новым методом на основе модели и позволяет точно и эффективно сегментировать все типы узловых образований, включая околоплевральные узлы, не отделяя легкое от окружающей области при компьютерной томографии (КТ) легких. В этом методе текстурная репрезентация изображения получается с помощью алгоритмов выделения текстурных признаков ткани и выбора признаков.

Результаты

Результаты. Предложенный метод был апробирован на 85 узелках из набора данных, полученных на базе иранской больницы Шариати. В этом обезличенном наборе сведений были представлены аннотации врачей и данные КТ. Результаты показывают, что предложенный алгоритм достигает среднего коэффициента сходства dice 84.75 %.

Заключение

Заключение. Представлен новый алгоритм для сегментации узелков в легком, который может сегментировать все типы узелков с высокой производительностью. Этот алгоритм основан на модели и вместе с алгоритмом активного контура способен повысить точность и устранить ложные срабатывания за счет определения начальной маски. Результаты сегментации легочных узелков на нормальном КТ-изображении следующие: precision 85.5 %, dice 85 %, accuracy 96 % и specificity 98 %.

Introduction

Introduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Computer processing of CT scans in the course of lung cancer diagnostics involves the following stages: medical image acquisition, pre-processing of medical images, segmentation, and false-positive reduction. Since segmentation is an essential stage in the process of medical image analysis, the development of novel segmentation approaches is attracting much research interest. Model-based segmentation approaches have recently gained in popularity, largely due to their potential to restore lost information.

Aim

Aim. To apply a texture appearance model for the segmentation of pulmonary nodules on computed tomography of the chest.

Materials and methods

Materials and methods. A novel model-based Texture Appearance Model (TAM) is proposed for precise and effective segmentation of all sorts of nodule regions. We taught the TAM for segmentation of a lung nodule in lung CT images using a combination of extracted texture characteristics from CT scans and Texture Representation of Image (TRI).

Results

Results. The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. The TAM was tested in 85 nodules from a dataset, yielding an average dice similarity coefficient (DSC) of 84.75 percent.

Conclusion

Conclusion. A novel method for segmenting nodules in the lung, which is capable of segmenting all forms of nodules with excellent accuracy, is proposed. This model-based technique, when used with the active loop algorithm, can enhance accuracy and decrease false positives by selecting the initial mask. The precision, dice, accuracy, and specificity of lung nodule segmentation on a normal CT scan are 85.5, 85, 96, and 98, which levels are superior to those produced by the Active Contour, Region Growing and the combination of Active Contour and Watershed algorithms.

модель внешнего вида текстуры (TAM)извлечение признаков текстурысистема автоматизированного обнаружения (CADs)компьютерная томография (CT)представление текстуры изображения (TRI)Texture Appearance Model (TAM)Texture Feature ExtractionComputer-Aided Detection system (CADs)Computed Tomography scan (CT)Texture Representation of Image (TRI)Исследование выполнено при финансовой поддержке РФФИ и ННФИ в рамках научного проекта № 20-57-56018. Результаты работы были получены с использованием вычислительных ресурсов суперкомпьютерного центра Санкт-Петербургского политехнического университета Петра Великого (www.scc.spbstu.ru).The study was carried out with the financial support of the Russian Foundation for Basic Research and National Science Foundation of the Islamic Republic of Iran as part of a scientific project № 20-57-56018. 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The authors declare that there are no conflicts of interest present.