Scientific publications and presentations
They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing.
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications
The proposed method has been developed in this research in order to construct hybrid method between HMRF and threshold. The paper uses Simple The ratio of the mean and variance of the image pixels are determined in order to obtain an optimum threshold value. Region merging after thresholding was carried out. The final output image was an image with tumor sections circled out. The segmentation adheres to boundaries and the procedure is fast and reproducible. Henry Nunoo-Mensah.
Richard Amedzrovi. A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure.
Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging.
Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor based on medical imaging results such as mammograms, x-ray computed tomography x-ray CT and magnetic resonance imaging MRI. We find that computer vision based techniques can identify tumors almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases. Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery.
There are various techniques for medical image segmentation.
Advanced Brain Tumour Segmentation from MRI Images
This paper presents a This paper presents a image segmentation technique for locating brain tumor Astrocytoma-A type of brain tumor. Proposed work has been divided in two phases-In the first phase MRI image database Astrocytoma grade I to IV is collected and then preprocessing is done to improve quality of image.
Second-phase includes three steps-Feature extraction, Feature selection and Image segmentation. ANFIS is a adaptive network which combines benefits of both fuzzy and neural network. Tumor boundary detection is one of the challenging tasks in the medical diagnosis field.
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The proposed work constructed brain tumor boundary using bi-modal fuzzy histogram thresholding and edge indication map EIM. The proposed work has The proposed work has two major steps. Initially step 1 is aimed to enhance the contrast in order to make the sharp edges. An intensity transformation is used for contrast enhancement with automatic threshold value produced by bimodal fuzzy histogram thresholding technique.
Next in step 2 the EIM is generated by hybrid approach with the results of existing edge operators and maximum voting scheme. The edge indication map produces continuous tumor boundary along with brain border and substructures cerebrospinal fluid CSF , sulcal CSF SCSF and interhemispheric fissure to reach the tumor location easily. The experimental results compared with gold standard using several evaluation parameters.
The results showed better values and quality to proposed method than the traditional edge detection techniques. The 3D volume construction using edge indication map is very useful to analysis the brain tumor location during the surgical planning process. Kalaiselvi T. Nagaraja P. Recently, Magnetic Resonance Imaging MRI of Brain is used widely in the clinical applications for the detection of abnormalities such as tumor.
Accurate segmentation of the affected regions in the brain MRI image plays a vital role in Accurate segmentation of the affected regions in the brain MRI image plays a vital role in the quantitative image analysis to detect the location of tumor in the brain. However, many segmentation algorithms suffer from limited accuracy, due to the presence of noise and intensity inhomogeneity in the brain MR images. This paper proposes a novel Textural Pixel Connectivity TPC based segmentation technique to predict the location of brain tumor.
Supervised learning-based multimodal MRI brain image analysis - The Lincoln Repository
If the image is classified as abnormal, then TPC segmentation process is applied for clustering out the background and tumor spot in the binary segmented output. Then, the growing pattern of tumor is analyzed and represented as a binary image output. The proposed technique achieves superior performance in terms of sensitivity, specificity, accuracy, error rate, correct rate, inconclusive rate, Positive Predicted Values PPV , Negative Predicted Values NPV , classified rate, prevalence, positive likelihood and negative likelihood, when compared to the traditional Adaboost and Enhanced Adaboost techniques.
Sankari and S. Wang Mengqiao,. September Lang, L.
Zhao and K. Mengqiao, Y. Jie, C. Yilei and W.
Swapnil R. Suganya, R. World Academy of Science, Engineering and Technology 50 International Journal of Bioscience, Biochemistry and Bioinformatics. Tanuja, M. Otsu, N. A threshold selection method from gray-level histograms. Kumar, Arun. K Uma Suhasini, V. December Indian Journal of Science and Technology, Vol 9 Janani, P. May Sundararaj GK, B.
Abdel- Maksoud E, E. March, Brain tumor segmentation using hybrid based clustering techniques. Egyptian Informatics Journal. Ananda RS, T. May, Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. Corso JJ, S. Efficient multilevel brain tumor segmentation with Integrated Bayesian model classification. Rao, B. Vijay and J. Liu, Ziwei, et al. Zheng, Shuai, et al. Girshick, Ross, et al. Havaei, Mohammad, et al. Dvorak, Pavel, and Bjoern Menze. Zikic, Darko, et al.