Muazzam Shehzad Quratulain Muazzam LEVEL SETS Muazzam Shehzad Quratulain Muazzam

HELLO! I am Muazzam Shehzad I am here because I love to give presentations. You can find me at @ muazzamnustian@gmail.com

Contents Image Segmentation Overview. Implicit & Explicit Concepts. Level Set Method overview. Drawbacks in old Techniques and Level Set advantages. How LS method Works? LS Equations Guassian Mixture Model Bhattacharyya based GMM Distance Related Researches and Implementation Applications

OVERVIEW Image Sementation-> using PDE( Power Differential Equations). Most influential PDE based image segmentation methods are based on active contours. Sub catogaries Region based segmentation method Edge based segmentation method Segmentation methods based on active contours(Level Set) which use global optimization terms for curve evolution, resulting in segmentation results which are more robust to noisy image data. Explicit vs. implicit contours why level sets?

Explicit vs. implicit Explicit interface representation one explicitly writes down the points that belong to the interface ∂Ω={−1,1}. Implicit interface representation defines the inter-face as the iso-contour of some functions

Continuous Space and Infinite-dimensional Optimization Motion of a hyper surface Evolution Eq. Energy minimization: E(C) min

Evolution of Explicit Boundaries

Evolution of Explicit Boundaries

Evolution of Explicit Boundaries

Statistical Learning of Explicit Shapes

Alignment of Explicit Contours

Segmentation with Statistical Shape Prior

Tracking with Kernel Shape Prior

Limitations of Explicit Representations

Why level Sets ?Advantages The explicit representation by default does not allow the evolving contour to undergo topological changes such that the segmentation of several objects or multiply-connected objects is not straight-forward. The segmentations obtained by a local optimization method are bound to depend on the initialization. The Snake algorithm is known to be quite sensitive to the initialization. For many realistic images, the segmentation algorithm tends to get stuck in undesired local minima—in particular in the presence of noise. The Snakes approach lacks a meaningful probabilistic interpretation. Extensions to other segmentation criteria—such as color, texture or motion—are not straight-forward. Active Contour /Snake need preprocessing of image(noise removal,intensity map). In Snake approach issues with narrow structures: big trouble in brain images.

HELLO! I am Quratulain I am here to share some knowledge on Level Sets(An Efficient Segmentation Method)

Level Set Method There is a surface, it intersects a plane, that gives us a contour With image segmentation, the surface is updated with forces derived from the image

The Level-Set Methods Existing level sets(LS) methods formulate the energy terms in Unsupervised manner. Supervised manner.

The level-set equation Image I / The level-set equation

Regularization term for level sets

Regularization

Gaussian Mixture Model Area term is found by this Do Cumulative minimization of distance between the training and testing distribution using different distance formulas e.g

Bhattacharyya based GMM Distance

Active Research & Implementation LEVEL SET SEGMENTATION OF DERMOSCOPY IMAGES

Cond… Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images.

Applications Computational physics Fluid mechanics Optimal design Computer Graphics Computer Vision. ………

References

THANKS! Any questions?