A Novel Statistical Approach to Obtain the Best Visibility Slice in MRI Sequence of Brain Tumors
Main Article Content
Abstract
Early diagnosis of brain tumor enhances the possibility of patients being cured. With the progress of the use of artificial intelligence in the medical field, the detection of brain tumors has become one of the researchers’ interests. Obtaining which slice in the MRI sequence gives the best visibility of the tumor is still a challenge. This paper introduced a novel statistical approach to extracting the tumor from the patient's MRI scan (sequence). Initially, the probability mass function (PMF) was computed for each image in the sequence. Then, the Kullback-Leibler divergence technique was applied to determine the tumor image(s) that diverged from the respective healthy ones. The best tumor visibility slice was determined using the root mean square error metric. In addition, a clustering approach was applied to segment the tumor images. The vector quantization (VQ) method was utilized for grouping the images into 16 different clusters, while a reverse VQ technique was employed to produce two-tone images. Finally, a 2D Teager operator was used to detect the edges for tumor demarcation. A private dataset of twenty MRI scans (sequences) was used for testing and evaluating the system.
Received 09/09/2023
Revised 01/12/2023
Accepted 03/12/2023
Published Online First 20/04/2024
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Thiyagarajan P, Padmanaban S, Thiruvenkadam K, Karuppanagounder S. Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends. Curr Med Imaging Rev. 2022. 18; 18(12): 1261 - 1275. https://doi.org/10.2174/1573405617666211215111937 .
Rehman MU, Ryu J, Nizami IF, Chong KT. RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames. Comput Biol Med. 2023; 152:106426. https://doi.org/10.1016/j.compbiomed.2022.106426 .
Al-Ani NQ, Al-Shamma O. A review on detecting brain tumors using deep learning and magnetic resonance images. Int J Electr Comput Eng. 2023; 13: 4582-4593. http://doi.org/10.11591/ijece.v13i4.pp4582-4593 .
Zhang K, Liu D. Customized Segment Anything Model for Medical Image Segmentation. arXiv.org. 2023. https://doi.org/10.48550/arXiv.2304.13785 .
Saha A, Banerjee S, Kurtek S, Narang S, Joon Sang Lee, Rao G, et al. demarcate: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. Neuro Imag: Clinic. 2016; 12: 132–43. https://doi.org/10.1016/j.nicl.2016.05.012 .
Sikandar S, Mahum R, Alsalman A. A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion. App Sci. 2023; 13(7): 4581. https://doi.org/10.3390/app13074581 .
El-Sherbiny B, Nabil N, El-Naby SH, Emad Y, Ayman N, Mohiy T, et al. BLB (Brain/Lung cancer detection and segmentation and Breast Dense calculation). 1st International Workshop on Deep and Representation Learning (IWDRL). IEEE Xplore. 2018: 41–7. https://doi.org/10.1109/IWDRL.2018.8358213 .
Amin J, Sharif M, Raza M, Saba T, Anjum MA. Brain tumor detection using statistical and machine learning method. Comput. Methods Programs Biomed. 2019; 177: 69–79. https://doi.org/10.1016/j.cmpb.2019.05.015 .
Bakshi A, Gupta A, Tanwar S, Sharma G, Bokoro PN, Alqahtani F, et al. Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression. Math. 2023; 11: 2364. https://doi.org/10.3390/math11102364 .
Dhole NV, Dixit VV. Review of brain tumor detection from MRI images with hybrid approaches. Multimed. Tools Appl. 2022; 81:10189–10220. https://doi.org/10.1007/s11042-022-12162-1 .
Soomro TA, Zheng L, Afifi AJ, Ali A, Soomro S, Yin M, et al. Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review. IEEE Rev Biomed Eng. 2022 ;16: 1–21. https://doi.org/10.1109/RBME.2022.3185292 .
Hasan AM, Qasim AF, Jalab HA, Ibrahim RW. Breast cancer MRI classification based on fractional entropy image enhancement and deep feature extraction. Baghdad Sci J. 2023; 20 (1): 0221-0221. https://doi.org/10.21123/bsj.2022.6782 .
Laith Alzubaidi, Bai J, Aiman Al-Sabaawi, Santamaría J, Albahri AS, Bashar, et al. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. J Big Data. 2023; 10: 46. https://doi.org/10.1186/s40537-023-00727-2 .
Gonzalez RC, Woods RE. Digital image processing. New York, Ny: Pearson; 2018. https://doi.org/10.4236/ijg.2014.55050 .
Wang C, Shen HW. Information Theory in Scientific Visualization. Entropy. 2011; 13: 254–73. http://dx.doi.org/10.3390/e13010254
Teager HM, Teager SM. Evidence for Nonlinear Sound Production Mechanisms in the Vocal Tract. Speech Production and Speech Modelling. 1990; 241–61. http://dx.doi.org/10.1007/978-94-009-2037-8_10.
Kumar BR, Joseph DK, Sreenivas TV. Teager energy-based blood cell segmentation. In: 14th International Conference on Digital Signal Processing Proceedings.; Santorini, Greece. 2002; 2: 619-622. https://doi.org/10.1109/ICDSP.2002.1028167 .
Abdel-Ouahab Boudraa, El. Image contrast enhancement based on 2D Teager-Kaiser operator. 15th IEEE International Conference on Image Processing. 2008. https://doi.org/10.1109/ICIP.2008.4712471 .
Zhang X, Qin Y, Li Y, Feng X, Li B, Hu X, et al. A GPR 2D Teager-Kaiser energy operator based on the multivariate variational mode decomposition. Remot Sens Lett. 2022; 14: 30–8. https://doi.org/10.3390/rs14194805 .
Yaseen BT, Kurnaz S, Ahmed SR. Detecting and Classifying Drug Interaction using Data mining Techniques. In: 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); Ankara, Turkey; 2022; 952-956. https://doi.org/10.1109/ISMSIT56059.2022.9932652 .
Naji NAR. Assessment of image quality of cervical spine complications using Three Magnetic Resonance Imaging Sequences. Baghdad Sci J. 2023; 20(3(Suppl.): 1155. https://doi.org/10.21123/bsj.2023.8244.