Title: Feature extraction of brain tumor MR Image by Local gradient pattern- and Local Binary pattern
Authors: Mr. S.Prakasha, Dr Channappa Bhyri, Dr C M Tawade, Dr Kalpana V
DOI: https://dx.doi.org/10.18535/jmscr/v11i12.21
Abstract
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient’s survival rate. MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction LGP and LBP approaches were used to classify the most common brain tumor types; Glioma and meningioma, Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to a Local Binary Pattern (LBP).
Keywords: LBP, Brain tumor, Local feature Descriptor, Recognition of pixel, Feature extraction.
References
- M. Sarhan, “Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform,” J Biomed Sci Eng, vol. 13, no. 06, pp. 102–112, 2020, doi: 10.4236/jbise.2020.136010.
- Yin et al.”New brain tumor classification method based on an improved version of whale optimization algorithm”, Biomedical Signal Processing and Control, Volume 56, February 2020, 101728.
- S. C. Chao et al., ‘‘A novel approach to overcome hypoxic tumor resistance: CU-ATSM-guided intensity-modulated radiation therapy,’’ Int. J. Radiat. Oncol. Biol. Phys., vol. 49, no. 4, pp. 1171–1182, Mar. 2001.
- L. Schwartz et al., ‘‘FDG-PET prediction of head and neck squamous cell cancer outcomes,’’ Arch. Otolaryngol. Head Neck Surg., vol. 130, no. 12, pp. 1361–1367, Dec. 2004.
- Chen, S. Wang, W. Fan, J. Sun, and S. Naoi, ‘‘Beyond human recognition: A CNN-based framework for handwritten character recognition,’’ in Proc. IEEE Asian Conf. Pattern Recognit. (IAPR), Nov. 2015, pp. 695–699
- Y. Liu, M. Chen, H. Ishikawa, G. Wollstein, J. S. Schuman, and J. M. Rehg, ‘‘Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding,’’ Med. Image Anal., vol. 15, no. 5, pp. 748–759, Oct. 2011.
- Sidibé et al., ‘‘An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images,’’ Comput. Methods Programs Biomed., vol. 139, pp. 109–117, Feb. 2017.
- S. Lee, A. J. Tyring, Y. Wu, A. Rokem, A. Y. Lee, and N. P. Deruyter, ‘‘Deep-learning based, automated segmentation of macular edema in optical coherence tomography,’’ Biomed. Opt. Express, vol. 8, no. 7, pp. 3440–3448, Jun. 2017.
- S. Kermany et al., ‘‘Identifying medical diagnoses and treatable diseases by image-based deep learning,’’ Cell, vol. 172, no. 5, pp. 1122–1131, Jun. 2018.
- Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
- Skibbe H, Reisert M. Circular fourier-hog features for rotation invariant object detection in biomedical images. 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012, 450-453.
- Ozturk Ş, Akdemir B. Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. Proc Comp Sci 2018; 132: 40-46.