1. Evaluation of Image Segmentation algorithms By Dr. Rajeev Srivastava

2. Contents Introduction Image segmentation algorithms Evaluation Metrics Result for segmentation

3. Introduction Segmentation subdivides the image into its constituents region or objects. The level to which the subdivides is carried depends on the problem being solved. Segmentation should stop when the object of interest in an application have been isolated. Segmentation method can be classified into two categories -: - In first category approach is to partition the images based on the abrupt changes in the intensities. -In second category partition an image into certain region which are similar according to certain criteria.

4. Image segmentation algorithm We will discuss following segmentation algorithm in the subsequent slides : Otsu,Edge based segmentation,K-means,fuzzyc-means,region-based method,snakes, contour based segmentation.

5. Otsu-segmentation

6. Otsu segmentation

7. Edge detection It is the most common approach for detecting the detecting the meaningful discontinuities in gray level. We will discuss the first and second order for detecting the edges. The magnitude of the first derivative can be used to detect the presence of an edge at a point in an image. The sign of second derivative can be used to determine whether an edge pixel lies on the dark or light side of an edge.

8. Edge Detection The two additional properties of second derivative are-: – It produces two value for every edge in an image. – Imaginary straight line joining the extreme positive and negative value of the second derivative would cross zero near the midpoint of the edge. The zero-crossing property of the second derivative is quite useful for locating the centres of thick edges.

9. Edge Detection

10. Robert mask Prewitt mask 0 01 0 10 000 111 01 01 01

11. Edge Detection Sobel mask -2 000 121 01 -202 01

12. Region Growing It is a procedure that groups pixels or sub region into larger regions based on predefined criteria. The basic approach is to start with a set of seed points and from these grow regions by appending to each seed those neighbouring pixels that have properties similar to the seeds. When a priori information is not available the procedure is to compute at every pixels the same set of properties that ultimately will be used to assign pixels to region during the growing process.

13. Region splitting and merging

15. K-means

17. Fuzzy-C-means

18. FuzzyCmeans Algorithm 1 Choose a number of clusters 2 Assign randomly to each point coefficient for being in the clusters. 3 Repeat until the algorithm has converged

19. Evaluation metrics Layout Entropy(H l ofE)-: E is a evaluation function based on information theory and the Minimum Description length Principle (MDL). Hl is defined as the entropy of the pixels in a segmentation layout. Segmentation layout is an image used to describe the result of segmentation. According to the Minimum description Length principle if we balance the trade-off between the uniformity of the individual regions with the complexity of the segmentation the minimum description length corresponds to the best segmentation.

20. Layout Entropy

21. Gray level uniformity

23. Evaluation method Ecw uses E inter to measure the inter-region colour difference, which is defined as the weighted proportion of pixels whose colour difference between its original colour and the average region colour in the other region is less that a pre-defined threshold. Note that, for a segmented image, a large value of intra-region visual error means plenty of pixels may be mistakenly merged and this image could have been undersegmented.

25. Intra region disparity quantifies the homogeneity of each region in the image. The global intra- region disparity is proportional to the number of pixels ri of each region Ri. The more a rcgion has an important number of pixels, the more it has an infiuence in the global intra.-region disparity. The region containing two different primitives must have a high intra-region disparity compared to the same region composed of one primitive.

27. F(I)

29. Discrepancy A discrepancy measure was based on the difference between the original and smoothed pictures. The measure proposed was the sum of the squared differences between gray levels of corresponding points in the original and smoothed pictures. If we assume that the image consists of objects and background, each having a specified distribution of gray levels, then we can compute, for any given threshold t, the Probability of misclassifying an object point as background, or vice versa.

30. Discrepancy

31. Result and Discussion The evaluation of segmentation algorithm is performed on mammographic images databases (such as DDISM) and texture image database. In order to evaluate various segmentation algorithms first we applied various segmentation algorithms on the images and evaluate various metrics based on the segmentation images.

32. Result and Discussion Texture image segmentation algorithm will require larger number of bits to specify the region id per pixel for the segmented image. Active contour produces the most uniform segmented images All the images generate the same degree of under-segmented images. Region growing shows the higher value of disparity value which suggest that segmented images produce by region growing are of better quality. DDISM Database OtsuK-meansFuzzy- C-means GaussianActive Countour TextureRegion Growing GraphCut Layout Entropy0.6331040.67320.67310.664030.66890.67980.65900.6727 Gray-level Uniformity 589715945958903797651049465969060336103416 E intra of Ecw0.5202 0.51380.51990.51950.51360.5202 20005282007256200745722314783004150258764979068562536798 125217125453125354138856172883166398338919151671 Discrepancy 1506113057167892351821703218163352121460

33. Result and Discussion Otsu present better segmentation result. The higher value of discrepancy of region growing suggest that larger number of background pixel are considered as object pixel.

34. Result and Discussion Fuzzy-c-means produces the most disorder segmented images which suggest it will require the larger number of bits to specify the region id per pixel. Region Growing produces the most uniform segmented images. All the segmented images produces the same degree of under- segmented images. Active contour has largest disparity value which suggest that segmented images produce by active contour is of better quality.

35. Result and Discussion Fuzzy-C-means produce the better segmentation result. Active Contour segmentation algorithm has largest value of discrepancy value which suggest that large number of background pixels are considered as object pixels.