Segmentation of skin lesion from digital images using texture distinctiveness

1. Segmentation of Skin
Lesions from Digital Images
using Texture
Distinctiveness
MEBIN P M
Guided by,
VESLY JOY
Presented by,

2. Contents
• Melanoma - Skin cancer
• Dermatoscope
• Segmentation algorithm
• Why TDLS ?
• TDLS Algorithm
• Experiment results
• Conclusion
• Reference

3. What is melanoma ?
• Melanoma is a potentially serious type of skin cancer in which
there is uncontrolled growth of melanocytes (pigment cells).
• Usually occurs on the trunk or lower extremities.
• Accounts for 75% of deaths associated with skin cancer.
• Incidence rate increases at rate of 3%.
• If detected early, the 5 year survival rate is 96%.

4. Dermatoscope
• Device used to look at skin lesions that acts as a filter &
magnifier.
• Images acquired through digital dermatoscope are referred to as
dermatoscopy images.
• Having low noise & consistent background illumination.
• Only 48% of dermatologists use dermatoscopes.
• High cost for screening.

5. Segmentation Algorithm
• Process of partitioning a digital image into multiple segments.
• Used to find the location of lesion border.
• Existing algorithms are only applicable to dermoscopy images.
• Digital photographs cannot be used with existing algorithms
because of illumination variation.
• Segmentation based on pixel color intensity.

6. Before After
Segmentation of image

7. Texture Distinctiveness Lesion
Segmentation (TDLS) Algorithm
 Segmentation based on texture information to locate lesion.
- Textures- smoothness,roughness or the presence of ridges,bumps etc
 Steps to correct shadows and bright spots caused by
illumination variation in digital photographs.
 Introduction of joint statistical TD metric and texture
based region classification.

8. TDLS Algorithm
● Consists of two main steps:
1. Learning of sparse texture distributions that represents skin and
lesion textures.
2. Calculation of TD metric.

9. Applying multistage illumination modeling to correct shadows.
Convert the corrected image to the XYZ color space.
* XYZ is not RGB, but approximately equal to RGB color space.
* Extrapolations of RGB, which are created mathematically.
Learn the sparse texture model.
* For each pixel s in image I, extract the texture vector to obtain the set of
texture vectors T.
𝑇 = { 𝑡𝑠𝑗 |1 ≤ 𝑗 ≤ 𝑁 × 𝑀 }
* A set of N x M texture vectors extracted. (N x M – pixel size)

10. Images with illumination Illumination corrected
images

11. ● Extracting the texture vector for a pixel in a single
channel

12.  Cluster the texture vectors in T, using k-means clustering
algorithm, to obtain the representative texture distributions.
1. K-means clustering algorithm.
Ck – kth set of texture vectors, μk – mean vector of kth set.
* Find K clusters that minimizes the sum of squared error between cluster members tsj
and cluster mean μk.t

13. * Limitation of k-means clustering is that does not take into account any
probabilistic information.
2. Apply finite mixture model clustering.
* To set the finite mixture model, the model parameters is in the set Θ are found to
maximize the log-likelihood function

14. * A Gaussian distribution is assumed for all clusters and the model parameters are
μ – distribution mean, Σ – distribution covariance, α – mixing proportion.
* Since we can’t find the solution for above equation analytically, we use expectation –
maximization algorithm.
* Expectation-maximization algorithm is initialized using cluster means, covariance and
mixing proportions based on the results of k-means clustering.
* Each texture vector is assigned to belong to the distribution which maximizes the
weighted probability

15.  Calculate probability that two texture distributions are distinct
using for all possible pairs of texture distributions(dj,k).
 Calculate the textural distinctiveness metric for
each texture distribution.
dj,k - probability that a texture distribution is distinct from another texture distribution.
P(Trk|I) - probability of occurrence of a pixel being associated with a texture distribution Trk.

16.  Apply the SRM algorithm to find the initial regions.
 Corrected lesion image is divided into a large number of regions using
statistical region merging (SRM) algorithm.
 In SRM pixels are sorted and merged based on their similarity with the
neighbouring pixel.
 Regions correspond to skin and lesion are obtained.
 Calculate the region distinctiveness metric DR for each
initial region.

17. •P(Trj |R) - probability of a pixel being associated with the jth texture distribution in region R.
 Calculate the threshold τ between the normal skin and lesion
classes.
•C1 (τ ) and C2 (τ ) – lesion and skin classes .
•σC(τ ) - variance of the TD.

18.  Classify each region as normal skin or lesion based on the
results.
 Apply a morphological dilation operator to the initial lesion
classification.
 Used to fill holes and smooth the border.
 For each contiguous region in the initial segmentation, count the
number of pixels in the region.

19. • Algorithm flowchart displaying the steps to learn the representative texture
distributions and calculate the TD metric
As the final lesion segmentation, return the contiguous
region consisting of the most pixels.

20. Experiment Results
TABLE I - SEGMENTATION ACCURACY RESULTS FOR ALL LESION PHOTOGRAPHS

21. Conclusion
• A novel lesion segmentation algorithm using
the concept of learning is proposed.
• TDLS algorithm captures dissimilarity between the texture
distribution.
• Then image is divided into smaller regions and classified
as lesion or skin based on TD map.
• The proposed framework produces the highest segmentation
accuracy using manually segmented images as ground truth.

22. • A larger data collection and annotation process, including
additional testing on a wide range of images, will be undertaken
as future work.
• Experimental results show that the proposed method is able
to segment the lesion in images of different scales and levels
of quality.

23. Reference
[1] N. Howlader, A. M. Noone, M. Krapcho, J. Garshell, N. Neyman, S. F. Altekruse, C. L. Kosary,
M. Yu, J. Ruhl, Z. Tatalovich, H. Cho, A. Mariotto, D. R. Lewis, H. S. Chen, E. J. Feuer, and K. A.
Cronin, “SEER cancer statistics review, 1975-2010,” Nat. Cancer Inst., Bethesda, MD, USA, Tech.
Rep., 2013
[2] A. F. Jerants, J. T. Johnson, C. D. Sheridan, and T. J. Caffrey, “Early detection and treatment of
skin cancer,” Amer. Family Phys., vol. 62, no. 2, pp. 1–6, Jul. 2000.
[3] Public Health Agency of Canada. (2013). Melanoma skin cancer. [Online].
Available:http://www.phac-aspc.gc.ca/cd-mc/cancer/melanoma skin cancer-cancer peau melanome-
eng.php
[4] A. Jemal, M. Saraiya, P. Patel, S. S. Cherala, J. Barnholtz-Sloan, J. Kim, C. L. Wiggins, and P. A.
Wingo, “Recent trends in cutaneous melanoma incidence and death rates in the united states, 1992-
2006,” J. Amer. Acad. Dermatol., vol. 65, no. 5, pp. S17.e1–S17.e11, Nov. 2011.

24. QUERIES

25. THANK YOU