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Neutrosophic sets and fuzzy c means clustering for improving ct liver image segmentation

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Neutrosophic sets and fuzzy c means clustering for improving ct liver image segmentation

  1. 1. NEUTROSOPHIC SETS AND FUZZY C- MEANS CLUSTERING FOR IMPROVING CT LIVER IMAGE SEGMENTATION By Ahmed Metwalli Anter, PHD Student IBICA2014 23-25/6/2014 – Ostrava Czech Republic Faculty of Computers & Information, Computer Science Dep., Mansoura University, Egypt
  2. 2. Scientific Research Group in Egypt www.egyptscience.net
  3. 3. Agenda  Introduction  Liver Segmentation Problems & challenging  Methodology  Proposed System Architecture  Experimental Results  Database Resources  Conclusion  Future Work
  4. 4. Introduction  Liver cancer is one of the major death factors in the world.  Early detection and accurate staging of liver cancer is an important issue in practical radiology.  Currently, the confirmed diagnosis used widely for the liver cancer is needle biopsy and it is an invasive technique and not recommended.
  5. 5. Introduction  Therefore, Computed Tomography (CT) has been identified as accurate and non-invasive imaging modalities in the diagnosis of hepatic lesions.  Manual segmentation of this CT scans are tedious and prohibitively time-consuming for a clinical setting.
  6. 6. Introduction  An application of abdominal CT imaging has been chosen and segmentation approach has been applied to see their ability and accuracy to segment abdominal CT images.  An improved segmentation approach based on Neutrosophic sets ( NS) and fuzzy c-mean clustering (FCM) is proposed.
  7. 7. Introduction  The abdominal CT image is transformed into NS domain, which is described using three subsets.  The percentage of truth in a subset T, the percentage of indeterminacy in a subset I, and the percentage of falsity in a subset F.  Threshold for subset T,I and F is adapted using Fuzzy C-mean algorithm.
  8. 8. Liver Segmentation: Problems and challenging  Automatic segmentation is a very challenging task due to the various factors:  CT abdominal images are represented in gray level rather than color.  Liver stretch over 150 slices in a CT image.  Irregularity in the liver shape and size between patients and the similarity with other organs making it harder to clearly identify the liver.  Indefinite shape of the lesions.  Low intensity contrast between lesions and similar to those of nearby tissues.
  9. 9. Cross section of abdomen region, liver has similar intensity with neighbour organs. Liver Segmentation: Problems and challenging  CT scan for patient has four phases and four orientations make segmentation very difficult.  Algorithms used to analyze abdominal CT can be both time consuming and error regions.
  10. 10. Methodology  Applied pre-processing median filter technique for CT liver images to enhance, remove noise that caused by defects of CT scanner and improve the quality.  Develop hybrid technique to segment liver from abdominal CT using:  Fuzzy C-mean algorithm (FCM)  Neutrosophic Sets and its operations  Connected Component Labeling algorithm(CCL)
  11. 11. Fuzzy C-mean algorithm (FCM)  FCM is an unsupervised clustering algorithm that has been successfully applied to a number of problems  FCM adopts fuzzy partitions to make each given value of data input between 0 and 1 in order to determine the degree of its belonging to a group.  FCM is a fuzzy clustering method allowing a piece of data to belong to two or more clusters.
  12. 12. Fuzzy C-mean algorithm (FCM)  Fuzzy set has been applied to handle uncertainty.  In applications such as expert system, Medical system, should consider not only the truth membership, but also the falsity membership and the indeterminacy of the two memberships.  It is hard for fuzzy set to solve such problems.
  13. 13. Neutrosophic Sets (NS)  Neutrosophy is a branch of philosophy, introduced by Florentin in 1995.  It can solve some problems that can not be solved by fuzzy logic.  NS values studies the propositions. Each proposition is estimated to have three components: the percentage of truth in a subset T, the percentage of indeterminacy in a subset I, and the percentage of falsity in a subset F
  14. 14. Neutrosophic Sets (NS)  The main distinction between neutrosophic logic (NL) and fuzzy logic (FL) is that the NL is a multiple value logic based on neutrosophy.  FL extends classical logic by assigning a membership function ranging in degree between 0 and 1 to variables.  Neutrosophic logic introduces a new component called “indeterminacy” and carries more information than fuzzy logic.
  15. 15. Neutrosophic Sets (NS) Example:  Ahmed wants to invite Karim to the lunch. Karim may or may not accept the invitation. In neutrosophic terms, the statement “Karim will accept the invitation” can be described in the following way: it is 60% true, 40% indeterminate, and 30% false.  Neutrosophic logic is close to human reasoning in the way that it considers the uncertain character of real life.
  16. 16. Neutrosophic Sets (NS) Convert Image to Neutrosophic  The image is transformed from image domain to neutrosophic domain.  Each pixel in the neutrosophic domain can be represented as T,I, and F which means the pixel is t% true, i% indeterminate and f% false.  The pixel P(i, j) in the image domain is transformed into neutrosophic domain PNS (i, j) ={T(i, j), I(i, j), F(i, j)}.
  17. 17.  T(i, j), I(i, j) and F(i, j) are defined as: is the local mean value of the pixels of the window size , Ho(i, j) is the homogeneity value of T at (i, j) which is described by the absolute value of difference between intensity g(i, j) and its local mean value Neutrosophic Sets (NS) Convert Image to Neutrosophic
  18. 18.  The image is divided into three parts: objects (O), edges ( E ), and background ( B ).  T(x, y) represents the degree of being an object pixel,  I (x, y) is the degree of being an edge pixel, and  F(x, y) is the degree of being a background pixel for pixel P(x, y).  This three parts are defined as follows: Neutrosophic Sets (NS) Convert Fuzzy NS to binary image
  19. 19. Neutrosophic Sets (NS) Convert Fuzzy NS to binary image Where tt, tf and ii are the thresholds computed from subsets {T,I,F} using adaptive FCM. The objects and background are mapped to 1, and the edges are mapped to 0 in the binary image.
  20. 20. Proposed hybrid NSFCM approach
  21. 21. Proposed approach Phases  The first phase searches for suitable slices in CT DICOM file because liver intensity distribution is different between slices. Liver parenchyma is the largest abdominal object in middle slices. These slices are suitable for segmentation and gives high accuracy.  The second phase pre-processing algorithm is used before the segmentation phase to enhance contrast, remove noise and emphasize certain features that affect segmentation algorithms and morphology operators.
  22. 22. Proposed Technique Phases  The third phase CT image is transformed into NS domain. Each pixel in the NS domain represented by T,I, and F which means the pixel is t% true, i% indeterminate and f % false.  In fourth phase binarize neutrosophic image PNS(T,I,F) using Fuzzy C-means algorithm.  Finally, the fifth phase is the post-processing CCL algorithm used to remove small objects, false positive regions and focused on liver parenchyma.
  23. 23. Proposed approach Algorithm
  24. 24. The proposed architecture of the proposed automatic liver segmentation approach
  25. 25. Experimental Results CT DataSet  CT dataset collected from radiopaedia divided into seven categories depends on the tumor type of benign and malignant, each of these categories have more than fifteen patients, each patient has more than one hundred fifty slices, and each patient has more than one phases of CT scan (arterial, delayed, portal venous, non-contrast), also this dataset has a diagnosis report for each patient.
  26. 26. Experimental Results  Hybrid technique based on Neutrosophic set and Fuzzy C-means clustering algorithm to segment liver automatically from abdominal CT is proposed.  A middle slice was selected as suitable abdominal CT image of a patient from DICOM file.  Pre-processing median filter3x3 window was used to enhance, remove noise and emphasize certain features that affect segmentation process.  The image is transformed into neutrosophic set based on T,I,F.
  27. 27. Experimental Results  NS image is enhanced using intensification transformation to improve the quality and emphasizes certain features of CT image to makes segmentation easier and more effective.  A new a adaptive threshold based on FCM are employed to reduce the indeterminacy degree of the image for three membership sets T, I and F.  NS CT image is converted to binary image based on threshold extracted from FCM for T, I and F.
  28. 28. Experimental Results  Finally, Post-processing CCL is applied with 8-connected objects to search for the largest connected region, remove false positive regions and focus on the ROI.
  29. 29. Experimental Results  The performance and accuracy of the proposed approach was evaluated by Jaccard Index and Dice coefficient techniques between automated segmented images and manual segmented images.  The proposed approach applied on 30 abdominal CT images  The accuracy obtained from Jaccard Index is 88% and accuracy obtained from Dice coefficient is 94% .
  30. 30. Experimental Results Pre-processing Noise removed using smoothing median filter. (a) The original CT slice. (b) The noise removed result. (a) (b)
  31. 31. Experimental Results Liver Segmentation Process Results of NS algorithm based on FCM for Liver segmnetation (a) Original,(b) T-domain, (c) F-domain, (d) Enhanced, (e) FCM for T-Image, (f) FCM for F-Image, (g) Homogeneity image, (h) Indeterminate image, (i) FCM for I-Image, (j) Binary Image based on T,I,F and finally (k) Segmentation of abdominal CT organs, and (l) Liver segmentation.
  32. 32. Results of NSFCM for segmenting noisy CT liver image (a) Original,(b) T-domain, (c) F-domain, (d) Enhanced, (e) FCM for T-Image, (f) FCM for F-Image, (g) Homogeneity image, (h) Indeterminate image, (i) FCM for I-Image, (j) Binary Image based on T,I,F , (k) Segmentation of abdominal CT organs, and (l) Liver segmentation. Experimental Results Liver Segmentation Process for Noisy Image
  33. 33.  The proposed NSFCM approach compared with some of existing methods such as classical fuzzy c-mean, local threshold and global threshold Otsu's method.  From experimental results, the proposed approach gives clear and well connected boundaries.  The result gives an improvement better than those obtained by other methods. Experimental Results Comparison between proposed technique with other methods
  34. 34. Comparison between proposed technique with other methods to segment noisy CT liver. (a) Original Image, (b) Fuzz C-mean Image, (c) Global Threshold - Otsu's, (d) Local Threshold, and (e) proposed NSFCM. Experimental Results Comparison between proposed technique with other methods
  35. 35. Experimental Results Segmentation accuracy NSFCM Segmentation accuracy for livers in terms of Jaccard Index and Dice Coefficient.
  36. 36. Conclusion  The presented approach for liver segmentation is able to reliably segment liver from abdominal CT in the used patient database  The image is described as a NS set using three membership sets T, I and F and thresholded using FCM.  The experiments on abdominal CT images with noise demonstrate that the proposed technique can reduce the indeterminate of the CT images and perform optimum threshold with better results especially in noisy cases.
  37. 37.  The experiments demonstrate that neutrosophy can reduce over-segmentation and gives better performance on noisy and non-uniform images than obtained by using other methods, since the proposed technique can handle uncertainty and indeterminacy better. Conclusion
  38. 38. Future Work  The proposed system can be improved by integrating Neutrosophic logic and Evolutionary algorithms to better manage inaccuracies.  We plan to assess the performance using a large dataset to evaluate generalization performance of the algorithm that includes a number of parameters in the feature measurement process, which means it might sensitive to size and characteristics of liver.

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