M.Tech / M.E / PhD Thesis | Electronics & Communication Engineering | India | Volume 5 Issue 8, August 2016
Brain Tumor Detection and its Area Measurement using K-Means Clustering baesd on Genetic Algorithm
Arnavi A. Patil | S. S. Badhe | A. A. Mulajkar
Abstract: Brain tumor detection and its analysis are tough tasks in medical image processing because brain image and its structure is complex that can be inspected only by expert radiologists. Segmentation plays an important role in the processing of medical images. MRI has become a particularly useful medical indicative tool for diagnosis of brain and other medical images. This paper presents a study of segmentation method implemented for tumor detection. The methods include optimized k-means clustering with genetic algorithm. Traditional k-means algorithm is sensitive to the initial cluster centers. Genetic k-means clustering techniques are used to detect tumor in MRI of brain images. At the end of process the tumor is diagnosed from the MRI image and its actual position and the shape are determined. The experimental results indicate that traditional k-means not only eliminate the over segmentation problem, but also provide fast and efficient clustering results.
Keywords: brain tumor, MRI image, segmentation, k-means clustering, Genetic algorithm
Edition: Volume 5 Issue 8, August 2016,
Pages: 26 - 31
How to Cite this Article?
Arnavi A. Patil, S. S. Badhe, A. A. Mulajkar, "Brain Tumor Detection and its Area Measurement using K-Means Clustering baesd on Genetic Algorithm", International Journal of Science and Research (IJSR), Volume 5 Issue 8, August 2016, pp. 26-31, https://www.ijsr.net/get_abstract.php?paper_id=ART2016724
How to Share this Article?
Similar Articles with Keyword 'brain tumor'
An Automated Detection and Segmentation of Tumor in Brain MRI using Machine Learning Technique
Brain Tumor Detection and Identification from T1 Post Contrast MR Images Using Cluster Based Segmentation
Gauri Anandgaonkar | Ganesh Sable