PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION
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The document presents three methods for tumor detection in MRI images: 1) K-means clustering with watershed algorithm, 2) Optimized K-means using genetic algorithm, and 3) Optimized C-means using genetic algorithm. It evaluates each method, finding that C-means clustering with genetic algorithm most accurately detects tumors by assigning data points to multiple clusters and finding the optimal solution in less time. The proposed approach successfully detects tumors with high accuracy, identifies the tumor area and internal structure, and provides a colorized output image.
PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION
- 2. www.company.com EFFICIENT SEGMENTATION METHODS FOR TUMOR DETECTION IN MRI IMAGES BY: S.Md. NOOR ZEBA KHANAM S.SAI SOWMYA G.PREETHI K.SRAVANTHI
- 3. www.company.com ABSTRACT Brain tumor extraction and its analysis are challenging tasks in Medical image processing because brain image is complicated. Segmentation plays a very important role in the medical image processing. In that way MRI (magnetic resonance imaging) has become a useful medical diagnostic tool for the diagnosis of brain & other medical images. In this project, we are presenting a comparative study of Three segmentation methods implemented for tumor detection. The methods include k-means clustering using watershed algorithm, optimized k-means and optimized c-means using genetic algorithm.
- 4. www.company.com INTRODUCTION • The BRAIN is the most important part of central nervous system. • The main task of the doctors is to detect the tumor which is a time consuming for which they feel burden. • Brain tumor is an intracranial solid neoplasm. • The only optimal solution for this problem is the use of ‘Image Segmentation’. Figure : Example of an MRI showing the presence of tumor in brain
- 5. www.company.com IMAGE SEGMENTATION • The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. • The segmentation might be grey level, colour, texture, depth or motion. • Example: ……
- 6. www.company.com EXISTING METHODS Fusion based : Overlapping the train image of the victim over a test image of same age group, thereby detecting the tumor. Demerits : The overlapping creates complexity due to different dimensions of both images. Time consuming process. Canny Based : To overcome the problem of detecting the edges, the better way is the use of Canny based edge detection. Demerits : Not support color images. This leads to increase in time to reach the optimal solution.
- 7. www.company.com PROPOSED METHOD The method include ‘k-means clustering +watershed, optimized k-means +genetic algorithm and optimized C- means +genetic algorithm’. At the end of process the tumor is extracted from the MRI image and also its exact position and shape are determined in colour.
- 8. www.company.com THEME OF PROPOSED METHOD K-means + watershed Optimized K-means + GA Optimized C-means + GA Successful detection + high accuracy + color.
- 9. www.company.com Clustering • Clustering is a process of collection of objects which are similar between them while dissimilar objects belong to other clusters. • A clustering technique is used to obtain a partition of N objects using a suitable measure such as resemblance function as a distance measure ‘d’.
- 10. www.company.com Region of interest Center of mass CLUSTERING PROCESS 10
- 11. www.company.com Region of interest Center of mass CLUSTERING PROCESS 11
- 12. www.company.com Region of interest Center of mass CLUSTERING PROCESS 12
- 13. www.company.com Figure : Clustering Technique Final Clusters
- 14. www.company.com K-means clustering k-means clustering aims to partition n observations into ‘K’ clusters in which each observation belongs to the cluster with the nearest mean. (a) original image (b) expert selection (c) K-means selection
- 15. www.company.com WATERSHED ALGORITHM • Watershed algorithm is used in image process primarily for segmentation purposes. • This algorithm can be used if the foreground and background of the image can be identified. MERITS: It works best to capture the weak edges. Watershed algorithm improves the primary results of segmentation of tumour done by k-means.
- 16. www.company.com K-means clustering with watershed Merits: If variables are huge, then K-Means most of the times computationally faster than, if we keep k small. Watershed algorithm improves the primary results of segmentation of tumour done by k-means. Demerits: Difficult to predict K-Value & k-means cannot find non- convex clusters. Different initial partitions can result in different final clusters. This method does not work well with clusters of different size and different density.
- 17. www.company.com C-means clustering • It is well known that the output of K-Means algorithm depends hardly on the initial seeds number as well as the final clusters number. • Therefore to avoid such obstacle FCM is suggested. • The fuzzy C-means relax the condition by allowing the feature vector to have multiple membership grades to multiple cluster. Figure: Result of Fuzzy C-means
- 18. www.company.com GENETIC ALGORITHM • The term genetic is derived from Greek word ‘genesis’ which means ‘to grow ‘or ‘to become’. • The implementation of Genetic algorithm begins with an initial population of chromosomes which are randomly selected. MERIT: It is the best optimizing tool. It gives best result when used with Fuzzy c-means clustering…
- 19. www.company.com C-means clustering with Genetic algorithm MERITS: This method considers only image intensity. Unlike k-means where data point must exclusively belong to one cluster center here data point is assigned to 2 or more clusters. DEMERITS: Aprior specification of the number of clusters. We get the better result but at the expense of more number of iteration.
- 20. www.company.com MAIN STRATEGY OF PROPOSED METHOD Proposed Method Tumor is detected with high accuracy Effectively detects the tumor area & internal Structure We get the resultant image in color C-means clustering + Genetic algorithm Here data point is assigned to 2 or more clusters GA gives best result in little time Best when works with C- means K-means Clustering + Watershed algorithm It is computationally faster, if we take K value small WA used to capture weak edges
- 21. www.company.com FUTURE SCOPE In terms of the near-future As Medical image segmentation plays a very important role in the field of image guided surgeries. By creating Three dimensional (3D) anatomical models from individual patients, training, planning, and computer guidance during surgery is improved.
- 22. www.company.com RESULTS: Fig.1.Results for first stage as K-means clustering. Fig.2.Results of Watershed algorithm
- 23. www.company.com RESULTS: Fig: Result of K-means and Watershed algorithm for one test image
- 24. www.company.com RESULTS: FIG: Resultant Image of C-means Clustering for cluster-1, cluster-2, cluster-3
- 25. www.company.com RESULT : FIG: Final MRI image for One Test image
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