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EFFICIENT SEGMENTATION
METHODS FOR TUMOR
DETECTION IN MRI IMAGES
BY:
S.Md. NOOR ZEBA KHANAM
S.SAI SOWMYA
G...
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ABSTRACT
 Brain tumor extraction and its analysis are challenging tasks
in Medical image processing becau...
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INTRODUCTION
• The BRAIN is the most important part of central nervous system.
• The main task of the doct...
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IMAGE SEGMENTATION
• The purpose of image segmentation is to partition an
image into meaningful regions wi...
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EXISTING METHODS
Fusion based : Overlapping the train image of the victim over a
test image of same age gr...
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PROPOSED METHOD
 The method include
‘k-means clustering +watershed,
optimized k-means +genetic algorithm
...
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THEME OF PROPOSED METHOD
K-means
+
watershed
Optimized
K-means
+
GA
Optimized
C-means
+
GA
Successful
dete...
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Clustering
• Clustering is a process of collection of objects which are
similar between them while dissimi...
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Region of
interest
Center of
mass
CLUSTERING PROCESS
10
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Region of
interest
Center of
mass
CLUSTERING PROCESS
11
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Region of
interest
Center of
mass
CLUSTERING PROCESS
12
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Figure : Clustering Technique
Final Clusters
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K-means clustering
k-means clustering aims to partition n observations into ‘K’
clusters in which each obs...
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WATERSHED ALGORITHM
• Watershed algorithm is used in image process primarily
for segmentation purposes.
• ...
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K-means clustering with watershed
Merits:
 If variables are huge, then K-Means most of the times
computat...
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C-means clustering
• It is well known that the output of K-Means algorithm
depends hardly on the initial s...
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GENETIC ALGORITHM
• The term genetic is derived from Greek word ‘genesis’
which means ‘to grow ‘or ‘to bec...
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C-means clustering with Genetic
algorithm
MERITS:
 This method considers only image intensity.
 Unlike k...
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MAIN STRATEGY OF PROPOSED
METHOD
Proposed Method
Tumor is detected with high
accuracy
Effectively detects ...
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FUTURE SCOPE
In terms of the near-future
 As Medical image segmentation plays a very important
role in th...
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RESULTS:
Fig.1.Results for first stage as K-means
clustering.
Fig.2.Results of Watershed algorithm
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RESULTS:
Fig: Result of K-means and Watershed algorithm for one test image
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RESULTS:
FIG: Resultant Image of
C-means Clustering for
cluster-1, cluster-2, cluster-3
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RESULT :
FIG: Final MRI image for One Test image
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PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION

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Efficient segmentation methods for the detection of tumor in MRI images................

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PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION

  1. 1. www.company.com
  2. 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. 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. 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. 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. 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. 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. 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. 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. 10. www.company.com Region of interest Center of mass CLUSTERING PROCESS 10
  11. 11. www.company.com Region of interest Center of mass CLUSTERING PROCESS 11
  12. 12. www.company.com Region of interest Center of mass CLUSTERING PROCESS 12
  13. 13. www.company.com Figure : Clustering Technique Final Clusters
  14. 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. 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. 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. 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. 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. 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. 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. 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. 22. www.company.com RESULTS: Fig.1.Results for first stage as K-means clustering. Fig.2.Results of Watershed algorithm
  23. 23. www.company.com RESULTS: Fig: Result of K-means and Watershed algorithm for one test image
  24. 24. www.company.com RESULTS: FIG: Resultant Image of C-means Clustering for cluster-1, cluster-2, cluster-3
  25. 25. www.company.com RESULT : FIG: Final MRI image for One Test image
  26. 26. www.company.com

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