A neural network model for object mask detection in medical images

Authors

  • Ihor Tereikovskyi National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
  • Oleksandr Korchenko University of Bielsko-Biala
  • Sergiy Bushuyev Kyiv National University of Construction and Architecture
  • Oleh Tereikovskyi National Aviation University
  • Ruslan Ziubina University of Bielsko-Biala
  • Olga Veselska University of Bielsko-Biala

Abstract

In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.

References

U. Adithya, C. Nagaraju, "Object Motion Direction Detection and Tracking for Automatic Video Surveillance", International Journal of Education and Management Engineering (IJEME), Vol.11, No.2, pp. 32-39, 2021. DOI: 10.5815/ijeme.2021.02.04.

B. Alpatov, P. Babayan. “Selection of moving objects in a sequence of multispectral images in the presence of geometrically distorted ones.” Herald of RGRTU. 2008. Issue 23. P. 18-25.

V. Badrinarayanan, A. Kendall, R. Cipolla. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” https://arxiv.org/pdf/1511.00561v2.pdf.

A. Bochkovskiy, C. Wang, H. Liao. “YOLOv4: Optimal Speed and Accuracy.” https://arxiv.org/pdf/2004.10934.pdf.

I. Deepa, A. Sharma, “Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization”, International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.3, pp. 32-46, 2022. DOI: 10.5815/ijigsp.2022.03.04.

D. Diwakar, D. Raj, “Recent Object Detection Techniques: A Survey”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.14, No.2, pp. 47-60, 2022. DOI: 10.5815/ijigsp.2022.02.05.

Z. Hengshuang, S. Jianping, Q. Xiaojuan, W. Xiaogang, J. Jiaya. “Pyramid Scene Parsing Network.” https://arxiv.org/pdf/1612.01105.pdf.

V. Hoai Viet, H. Nhat Duy, “Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.14, No.1, pp. 64-81, 2022.DOI: 10.5815/ijigsp.2022.01.06.

A. Horytov, S. Yakovchenko. “Selection of parametrically defined objects on a low-resolution image.” Management, computing and informatics. 2017. No. 2. P.88-90.

Z. Hu, I. Tereykovskiy, Y. Zorin, L. Tereykovska, A. Zhibek “Optimization of convolutional neural network structure for biometric authentication by face geometry.” Advances in Intelligent Systems and Computing. 2019. Vol. 754. P. 567-577.

T. Kong, F. Sun, H. Liu, Y. Jiang, L. Li, J. Shi, FoveaBox: Beyound Anchor-Based Object Detection, IEEE Trans. Image Process. 29 (2020) 7389–7398. https://doi.org/10.1109/TIP.2020.3002345.

Y. LeCun et al. “Learning Hierarchical Features for Scene Labeling.” URL: http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf (access date: 02/02/2017).

V. Muraviev “Models and algorithms of image processing and analysis for systems of automatic tracking of aerial objects.” author's review. diss. for the application of scientific degrees of candidate of technical sciences: special. 05.13.01 - system analysis, management and processing. Ryazan 2010. 17 p.

P. Oniskiv, Y. Lytvynenko “Analysis of image segmentation methods”. Theoretical and applied aspects of radio engineering, instrument engineering and computer technologies: materials of IV all-Ukrainian. science and technology conf. 2019. P.48-49.

D. Perfil`ev “Segmentation Object Strategy on Digital Image”. Journal of Siberian Federal University. Engineering & Technologies. 2018. No. 11(2). R. 213-220.

O. Ronneberger, P. Fischer, T. Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” https://arxiv.org/abs/1505.04597.

J. Shen, “Motion detection in color image sequence and shadow elimination.” Visual Communications and Image Processing. 2014. Vol. 5308. P. 731-740.

O. Shkurat “Methods and information technology of processing archival medical images.” dissertation. ... candidate technical Sciences: 05.13.06. K., 2020. 211 p.

N. Stulov “Algorithms for the selection of basic features and methods of formation invariant to rotation, transfer, and rescaling of features of objects.” autoref. diss. for the application of scientific degrees of candidate of technical sciences: special. 05.13.01 - system analysis, management and processing. Vladimir 2006. 16 p.

I. Tereikovskyi, I. Subach, O. Tereikovskyi, L. Tereikovska, S.Toliupa, V. Nakonechnyi “Parameter Definition for Multilayer Perceptron Intended for Speaker Identification.” IEEE International Conference on Advanced Trends in Information Theory. Kyiv, Ukraine. 2019. P. 227-231.

S. Toliupa, Y. Kulakov, I. Tereikovskyi, O. Tereikovskyi, L. Tereikovska, V. Nakonechnyi “Keyboard Dynamic Analysis by Alexnet Type Neural Network.” IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering. 2020. P. 416-420.

S. Toliupa, I. Tereikovskiy, I. Dychka, L. Tereikovska, and A. Trush, "The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry," 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), 2019, pp. 323-327, DOI: 10.1109/AIACT.2019.8847847.

H. Wang, X. Wang, L. Yu, and F. Zhong, "Design of Mean Shift Tracking Algorithm Based on Target Position Prediction," 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2019, pp. 1114-1119, doi:10.1109/ICMA.2019.8816295.

Yudin O., Toliupa S., Korchenko O., Tereikovska L., Tereikovskyi I., Tereikovskyi O. “Determination of Signs of Information and Psychological Influence in the Tone of Sound Sequences”. IEEE 2nd International Conference on Advanced Trends in Information Theory. 2020, pp. 276-280.

S. Zhang, L. Wen, X. Bian, Z. Lei, S.Z. Li, Single-Shot Refinement Neural Network for Object Detection, in 2018: pp. 4203-4212.

Downloads

Published

2024-04-19

Issue

Section

Biomedical Engineering