1. Home
  2. Vol 5, No 4 (2024)
  3. Kuklin

Technique for Segmentation and Selection of Biomedical Images

Vladimir Zh. Kuklin, Leonid M. Chervyakov, Andrei N. Ruslantsev, Tagirbek G. Aslanov

Abstract


Various research fields, such as biology and medicine, have increasingly used techniques for the machine estimation of generated pulses. For example, in biological medicine, classifying information makes it possible to automate the interpretation of incoming data obtained owing to diagnosis, which is necessary when processing large volumes of information arrays. This study aims to develop algorithms that enable the selection of single-type objects in images with subsequent image splitting into a set list of segments with heterogeneous tension, even when the number of reference images is very small. Analysis of existing algorithms allowed us to determine the focus area for developing and improving the efficiency of the algorithms. The existing algorithms show poor performance results in analyzing the dark parts of images, so it makes sense to develop an algorithm for image intensity normalization. The developed algorithm simplifies the procedure of partitioning the training base for the classifier owing to the use of the feature vectors. A random forest algorithm was used for image classification, followed by boundary refinement using a Markov field. The image-splitting algorithm precisely separates parts of the brain structure by applying a Markov field to refine classification results. The proposed classification algorithm showed strong results in comparison with existing algorithms, particularly in the comparison of the Dice criterion. The proposed method shows an average increase of 10% in classification accuracy. One way to improve the presented algorithm is to add texture elements to the feature vector, which allows the identification of distinguishing features of the elements, such as shape and length, which could improve this algorithm for a more accurate classification of substructures.

 

Doi: 10.28991/HEF-2024-05-04-011

Full Text: PDF


Keywords


Image Segmentation; Image Splitting; Medical Biology; Brain Structure; Tension.

References


Fawzi, A., Achuthan, A., & Belaton, B. (2021). Brain image segmentation in recent years: A narrative review. Brain Sciences, 11(8), 1055. doi:10.3390/brainsci11081055.

Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017). Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. Journal of Digital Imaging, 30(4), 449–459. doi:10.1007/s10278-017-9983-4.

Marchesin, S., Giachelle, F., Marini, N., Atzori, M., Boytcheva, S., Buttafuoco, G., Ciompi, F., Di Nunzio, G. M., Fraggetta, F., Irrera, O., Müller, H., Primov, T., Vatrano, S., & Silvello, G. (2022). Empowering digital pathology applications through explainable knowledge extraction tools. Journal of Pathology Informatics, 13, 100139. doi:10.1016/j.jpi.2022.100139.

Moser, E., Stadlbauer, A., Windischberger, C., Quick, H. H., & Ladd, M. E. (2009). Magnetic resonance imaging methodology. European Journal of Nuclear Medicine and Molecular Imaging, 36(SUPPL. 1), 30–41. doi:10.1007/s00259-008-0938-3.

Chen, J. E., & Glover, G. H. (2015). Functional Magnetic Resonance Imaging Methods. Neuropsychology Review, 25(3), 289–313. doi:10.1007/s11065-015-9294-9.

Budde, M. D., & Frank, J. A. (2012). Examining brain microstructure using structure tensor analysis of histological sections. NeuroImage, 63(1), 1–10. doi:10.1016/j.neuroimage.2012.06.042.

Polezhaev, D. V., Tatarkanov, A. A., & Alexandrov, I. A. (2023). Forming the Architecture of a Multi-Layered Model of Physical Data Storage for Complex Telemedicine Systems. HighTech and Innovation Journal, 4(4), 797–810. doi:10.28991/HIJ-2023-04-04-09.

Giger, M., & MacMahon, H. (1996). Image processing and computer-aided diagnosis. Radiologic Clinics of North America, 34(3), 565–596. doi:10.1016/s0033-8389(22)00492-4.

Bozek, J., Mustra, M., Delac, K., & Grgic, M. (2009). A survey of image processing algorithms in digital mammography. Studies in Computational Intelligence, 231, 631–657. doi:10.1007/978-3-642-02900-4_24.

Ghaheri, A., Shoar, S., Naderan, M., & Hoseini, S. S. (2015). The applications of genetic algorithms in medicine. Oman Medical Journal, 30(6), 406–416. doi:10.5001/omj.2015.82.

Jiao, L., & Zhao, J. (2019). A Survey on the New Generation of Deep Learning in Image Processing. IEEE Access, 7, 172231–172263. doi:10.1109/ACCESS.2019.2956508.

Das, S., & Konar, A. (2009). Automatic image pixel clustering with an improved differential evolution. Applied Soft Computing Journal, 9(1), 226–236. doi:10.1016/j.asoc.2007.12.008.

Yin, H., Varava, A., & Kragic, D. (2021). Modeling, learning, perception, and control methods for deformable object manipulation. Science Robotics, 6(54), eabd8803. doi:10.1126/scirobotics.abd8803.

Borodin, A. A., Kabulova, E. G., & Polozhentsev, K. A. (2016). Video detection of problems in the melting of consumable electrodes in a vacuum arc furnace. Steel in Translation, 46(5), 322–324. doi:10.3103/S0967091216050041.

Zhang, J., Zhong, Y., & Gu, C. (2018). Deformable Models for Surgical Simulation: A Survey. IEEE Reviews in Biomedical Engineering, 11, 143–164. doi:10.1109/RBME.2017.2773521.

Huang, X., Liu, Y., Li, Y., Qi, K., Gao, A., Zheng, B., Liang, D., & Long, X. (2023). Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs. Sensors, 23(2), 655. doi:10.3390/s23020655.

Yousaf, F., Iqbal, S., Fatima, N., Kousar, T., & Shafry Mohd Rahim, M. (2023). Multi-class disease detection using deep learning and human brain medical imaging. Biomedical Signal Processing and Control, 85, 104875. doi:10.1016/j.bspc.2023.104875.

Pan, S., Liu, X., Xie, N., & Chong, Y. (2023). EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation. BMC Bioinformatics, 24(1), 85. doi:10.1186/s12859-023-05196-1.

Trombini, M., Solarna, D., Moser, G., & Dellepiane, S. (2023). A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields. Pattern Recognition, 134, 109082. doi:10.1016/j.patcog.2022.109082.

Fernando, K. R. M., & Tsokos, C. P. (2023). Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation. Information Fusion, 92, 450–465. doi:10.1016/j.inffus.2022.12.013.

Qiu, B., Wang, Z., Tang, Z., Chen, C., Fan, Z., & Li, W. (2016). Automated cropping intensity extraction from isolines of wavelet spectra. Computers and Electronics in Agriculture, 125, 1–11. doi:10.1016/j.compag.2016.04.015.

Gembris, D., Taylor, J. G., Schor, S., Frings, W., Suter, D., & Posse, S. (2000). Functional magnetic resonance imaging in real time (FIRE): Sliding- window correlation analysis and reference-vector optimization. Magnetic Resonance in Medicine, 43(2), 259–268. doi:10.1002/(SICI)1522-2594(200002)43:2<259::AID-MRM13>3.0.CO;2-P.

Barmak, A. V., Krak, Y. V., Manziuk, E. A., & Kasianiuk, V. S. (2019). Information technologyof separating hyperplanes synthesisfor linear classifiers. Journal of Automation and Information Sciences, 51(5), 54–64. doi:10.1615/JAutomatInfScien.v51.i5.50.

Schölkopf, B., Mika, S., Burges, C. J. C., Knirsch, P., Müller, K. R., Rätsch, G., & Smola, A. J. (1999). Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5), 1000–1017. doi:10.1109/72.788641.

Alexandrov, I. A., Kirichek, A. V., Kuklin, V. Z., & Chervyakov, L. M. (2023). Development of an Algorithm for Multicriteria Optimization of Deep Learning Neural Networks. HighTech and Innovation Journal, 4(1), 157–173. doi:10.28991/HIJ-2023-04-01-011.

Petro, A. B., Sbert, C., & Morel, J.-M. (2014). Multiscale Retinex. Image Processing On Line, 4, 71–88. doi:10.5201/ipol.2014.107.

Akbas, E., & Ahuja, N. (2020). Low-level multiscale image segmentation and a benchmark for its evaluation. Computer Vision and Image Understanding, 199, 103026. doi:10.1016/j.cviu.2020.103026.

Veksler, O., Boykov, Y., & Mehrani, P. (2010). Superpixels and supervoxels in an energy optimization framework. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 6315 LNCS, 211–224. doi:10.1007/978-3-642-15555-0_16.

Ghassabeh, Y. A., & Rudzicz, F. (2018). Modified mean shift algorithm. IET Image Processing, 12(12), 2172–2177. doi:10.1049/iet-ipr.2018.5600.

Bechar, M. E. A., Settouti, N., Barra, V., & Chikh, M. A. (2018). Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease. Multidimensional Systems and Signal Processing, 29(3), 979–998. doi:10.1007/s11045-017-0483-y.

Xu, G., Liu, M., Jiang, Z., Söffker, D., & Shen, W. (2019). Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors (Switzerland), 19(5), 1088. doi:10.3390/s19051088.

Minciullo, L., Bromiley, P. A., Felson, D. T., & Cootes, T. F. (2017). Indecisive trees for classification and prediction of knee osteoarthritis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10541 LNCS, 283–290. Springer. doi:10.1007/978-3-319-67389-9_33.

Thomas, T., P. Vijayaraghavan, A., & Emmanuel, S. (2020). Applications of Decision Trees. Machine Learning Approaches in Cyber Security Analytics, 157–184. doi:10.1007/978-981-15-1706-8_9.

Ben naceur, M., Akil, M., Saouli, R., & Kachouri, R. (2020). Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical Image Analysis, 63, 101692. doi:10.1016/j.media.2020.101692.

Ellwaa, A., Hussein, A., AlNaggar, E., Zidan, M., Zaki, M., Ismail, M. A., & Ghanem, N. M. (2016). Brain tumor segmantation using random forest trained on iteratively selected patients. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10154 LNCS, 129–137. Springer. doi:10.1007/978-3-319-55524-9_13.

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P. M., & Larochelle, H. (2017). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18–31. doi:10.1016/j.media.2016.05.004.

Biratu, E. S., Schwenker, F., Debelee, T. G., Kebede, S. R., Negera, W. G., & Molla, H. T. (2021). Enhanced region growing for brain tumor MR image segmentation. Journal of Imaging, 7(2), 22. doi:10.3390/jimaging7020022.


Full Text: PDF

DOI: 10.28991/HEF-2024-05-04-011

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Vladimir Zh. Kuklin, Leonid M. Chervyakov, Andrei N. Ruslantsev, Tagirbek G. Aslanov