Brain Tumor Detection and Tumor Area Calculation by Means of Morphological Processes and Edge Detection Methods
Öz
Nowadays, magnetic resonance imaging is one of the imaging tools
commonly used in the field of medical image processing. These images can be
used to diagnose many diseases. These images are used for the diagnosis of
tumors especially in brain MR images. In this study, some image processing
techniques were applied to MR images in order to locate and measure the size of
brain tumors. First, sharpening and filtering were performed to improve image
quality. Edge detection methods are used to determine the location of the
tumor. Edge detection in this study is an important step for tumor detection.
Then, the tumor area was obtained by morphological procedures. Thus, the
location of the tumor was found and the area was calculated.
Anahtar Kelimeler
Area Calculation Brain Tumor Detection Edge Detection Methods Magnetic Resonance Imaging Morphological Processes
Kaynakça
- N. Manasa, G. Mounica, and B. Divya Tejaswi. "Brain Tumor Detection Based on Canny Edge Detection Algorithm and it’s area calculation." Brain, 2016.
- Md R Islam and Md R. Imteaz. "Detection and analysis of brain tumor from MRI by Integrated Thresholding and Morphological Process with Histogram based method." in 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE, 2018.
- A. Aslam, E. Khan, M. M. S. Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation”, Elsevier, ScienceDirect, Procedia Computer science , pp. 430-437, 2015.
- R. G. Selkar, and M. N. Thakare. "Brain tumor detection and segmentation by using thresholding and watershed algorithm." International Journal of Advanced Information and Communication Technology 1.3 321-4. 2014.
- P. Dhage, M. R. Phegade, and S. K. Shah. "Watershed segmentation brain tumor detection." Pervasive Computing (ICPC), 2015 International Conference on. IEEE, 2015.
- S. Pereira, A. Pinto, V Alves and C. A. Silva. “Brain tumor segmentation using convolutional neural networks in MRI images”. IEEE transactions on medical imaging, 35(5), 1240-1251. 2016.
- M. Rezaei, H. Yang, and C. Meinel. "Brain Abnormality Detection by Deep Convolutional Neural Network." arXiv preprint arXiv:1708.05206. 2017.
- TS. D. Murthy and G. Sadashivappa, “Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor”. In Advances in Electronics, Computers and Communications (ICAECC), 2014 International Conference on (pp. 1-6). IEEE, 2014, October.
- T. D. Vishnumurthy, H. S. Mohana, and V. A. Meshram. “Automatic segmentation of brain MRI images and tumor detection using morphological techniques”. in Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 016 International Conference on (pp. 6-11). IEEE, 2016, December.
- E. E. Ulku, and A. Y. Camurcu, “Computer aided brain tumor detection with histogram equalization and morphological image processing techniques”. In Electronics, Computer and Computation (ICECCO), 2013 International Conference on (pp. 48-51). IEEE, 2013, November.
- S. S. Gawande and V. Mendre. "Brain tumor diagnosis using image processing: A survey." Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017 2nd IEEE International Conference on. IEEE, 2017.
- K. Parvati, B. S. Prakasa Rao, and M. Mariya Das,” Image Segmentation Using Gray-Scale Morphology and MarkerControlled Watershed Transformation”, Hindawi Publishing Corporation, Discrete Dynamics in Nature and Society Volume 2008, Article ID 384346, doi:10.1155/2008/384346
Morfolojik İşlemler ve Kenar Algılama Yöntemler Vasıtasıyla Beyin Tümör Yeri Tespiti ve Tümör Alan Hesabının Yapılması
Öz
Günümüzde Manyetik Rezonans Görüntüleme (MRG), tıbbi
görüntü işleme alanında sıkça kullanılan görüntüleme araçlarından biridir.
Birçok hastalık teşhisi için bu görüntülerden faydalanılabilmektedir. Beyin MR
görüntülerinde özellikle tümör teşhisi için bu görüntülere başvurulmaktadır. Bu
çalışmada, beyin tümörlerinin yerini tespit etmek ve büyüklüğünü ölçmek için MR
görüntülerine bazı görüntü işleme teknikleri uygulanmıştır. Öncelikle, görüntü
kalitesini artırmak için keskinleştirme ve filtreleme işlemleri
gerçekleştirilmiştir. Tümör yerini belirlemek için kenar algılama yöntemlerine
yer verilmiştir. Bu çalışma içerisindeki kenar algılama işlemi, tümör tespiti
için önemli bir adımı oluşturmaktadır. Daha sonra morfolojik işlemler ile tümör
alanı elde edilmiştir. Böylece tümörün yeri bulunmuş ve alanı hesaplanmıştır.
Anahtar Kelimeler
Alan Hesabı Beyin Tümör Tespiti Kenar Algılama Yöntemleri Manyetik Rezonans Görüntüleme Morfolojik İşlemler
Kaynakça
- N. Manasa, G. Mounica, and B. Divya Tejaswi. "Brain Tumor Detection Based on Canny Edge Detection Algorithm and it’s area calculation." Brain, 2016.
- Md R Islam and Md R. Imteaz. "Detection and analysis of brain tumor from MRI by Integrated Thresholding and Morphological Process with Histogram based method." in 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE, 2018.
- A. Aslam, E. Khan, M. M. S. Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation”, Elsevier, ScienceDirect, Procedia Computer science , pp. 430-437, 2015.
- R. G. Selkar, and M. N. Thakare. "Brain tumor detection and segmentation by using thresholding and watershed algorithm." International Journal of Advanced Information and Communication Technology 1.3 321-4. 2014.
- P. Dhage, M. R. Phegade, and S. K. Shah. "Watershed segmentation brain tumor detection." Pervasive Computing (ICPC), 2015 International Conference on. IEEE, 2015.
- S. Pereira, A. Pinto, V Alves and C. A. Silva. “Brain tumor segmentation using convolutional neural networks in MRI images”. IEEE transactions on medical imaging, 35(5), 1240-1251. 2016.
- M. Rezaei, H. Yang, and C. Meinel. "Brain Abnormality Detection by Deep Convolutional Neural Network." arXiv preprint arXiv:1708.05206. 2017.
- TS. D. Murthy and G. Sadashivappa, “Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor”. In Advances in Electronics, Computers and Communications (ICAECC), 2014 International Conference on (pp. 1-6). IEEE, 2014, October.
- T. D. Vishnumurthy, H. S. Mohana, and V. A. Meshram. “Automatic segmentation of brain MRI images and tumor detection using morphological techniques”. in Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 016 International Conference on (pp. 6-11). IEEE, 2016, December.
- E. E. Ulku, and A. Y. Camurcu, “Computer aided brain tumor detection with histogram equalization and morphological image processing techniques”. In Electronics, Computer and Computation (ICECCO), 2013 International Conference on (pp. 48-51). IEEE, 2013, November.
- S. S. Gawande and V. Mendre. "Brain tumor diagnosis using image processing: A survey." Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017 2nd IEEE International Conference on. IEEE, 2017.
- K. Parvati, B. S. Prakasa Rao, and M. Mariya Das,” Image Segmentation Using Gray-Scale Morphology and MarkerControlled Watershed Transformation”, Hindawi Publishing Corporation, Discrete Dynamics in Nature and Society Volume 2008, Article ID 384346, doi:10.1155/2008/384346
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 5 Aralık 2018 |
| Gönderilme Tarihi | 15 Kasım 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 2 Sayı: 2 |
