Segmentation Based on Propagation of Dynamically Changing Superpixels; Programming and Computer Software; Vol. 46, iss. 3
| Parent link: | Programming and Computer Software Vol. 46, iss. 3.— 2020.— [P. 195-206] |
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| Autor Corporativo: | |
| Outros Autores: | , , , , |
| Resumo: | Title screen This paper describes a new method for medical data segmentation based on superpixel propagation. The proposed method is a modification of the classical region growing algorithm and partly inherits the concept of octrees. The key difference of the proposed approach is the transition to the superpixel domain, as well as more flexible conditions for adding neighbor superpixels to the region. The region formation algorithm checks superpixels for compliance with some homogeneity criteria. First, the average intensity of superpixels is compared with the intensity of a resulting region. Second, each pixel on the edges and diagonals of a superpixel is compared with a threshold value. An important feature of the proposed method is the dynamically changing (floating) size of superpixels. The resulting region is formed by constructing a spline based on the points of intersection among the superpixels external to the region. To test the accuracy of the method, we use the MRI images of the left ventricle obtained at the University of York and MRI images of brain tumors obtained at the Southern Medical University. To demonstrate the performance of our method, a set of high-resolution synthetic images was additionally created. As an accuracy estimation metric, we use the Dice similarity coefficient (DSC). For the proposed method, it corresponds to 0.93 ± 0.03 and 0.89 ± 0.07 for the left ventricle and tumor segmentation, respectively. It is demonstrated that a step-by-step reduction in the size of a superpixel can significantly speed up the method without loss of accuracy. Режим доступа: по договору с организацией-держателем ресурса |
| Idioma: | inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1134/S0361768820030044 |
| Formato: | Recurso Electrónico Capítulo de Livro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662244 |
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| 101 | 0 | |a eng | |
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| 181 | 0 | |a i | |
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| 200 | 1 | |a Segmentation Based on Propagation of Dynamically Changing Superpixels |f V. V. Danilov, O. M. Gerget, I. P. Skirnevsky [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 29 tit.] | ||
| 330 | |a This paper describes a new method for medical data segmentation based on superpixel propagation. The proposed method is a modification of the classical region growing algorithm and partly inherits the concept of octrees. The key difference of the proposed approach is the transition to the superpixel domain, as well as more flexible conditions for adding neighbor superpixels to the region. The region formation algorithm checks superpixels for compliance with some homogeneity criteria. First, the average intensity of superpixels is compared with the intensity of a resulting region. Second, each pixel on the edges and diagonals of a superpixel is compared with a threshold value. An important feature of the proposed method is the dynamically changing (floating) size of superpixels. The resulting region is formed by constructing a spline based on the points of intersection among the superpixels external to the region. To test the accuracy of the method, we use the MRI images of the left ventricle obtained at the University of York and MRI images of brain tumors obtained at the Southern Medical University. To demonstrate the performance of our method, a set of high-resolution synthetic images was additionally created. As an accuracy estimation metric, we use the Dice similarity coefficient (DSC). For the proposed method, it corresponds to 0.93 ± 0.03 and 0.89 ± 0.07 for the left ventricle and tumor segmentation, respectively. It is demonstrated that a step-by-step reduction in the size of a superpixel can significantly speed up the method without loss of accuracy. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Programming and Computer Software | ||
| 463 | |t Vol. 46, iss. 3 |v [P. 195-206] |d 2020 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a пиксели | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a опухоли | |
| 701 | 1 | |a Danilov |b V. V. |c specialist in the field of informatics and computer technology |c engineer of Tomsk Polytechnic University |f 1989- |g Vyacheslav Vladimirovich |3 (RuTPU)RU\TPU\pers\37831 | |
| 701 | 1 | |a Gerget |b O. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, Doctor of Sciences |f 1974- |g Olga Mikhailovna |3 (RuTPU)RU\TPU\pers\31430 |9 15593 | |
| 701 | 1 | |a Skirnevsky |b I. P. |c specialist in the field of automation and computer systems |c educational master Tomsk Polytechnic University |f 1989- |g Igor Petrovich |3 (RuTPU)RU\TPU\pers\35105 | |
| 701 | 1 | |a Manakov |b R. |g Roman | |
| 701 | 1 | |a Kolpashchikov |b D. Yu. |g Dmitry Yurjevich | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 2 | |a RU |b 63413507 |c 20200618 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1134/S0361768820030044 | |
| 942 | |c CF | ||