Multi-Resolution Texture-Based 3D Level Set Segmentation

Toward Texture-Based 3D Level Set Image Segmentation

This paper presents a three-dimensional level set-based image segmentation method. Instead of the typical image features, like intensity or edge information, the method uses texture feature analysis in order to be more applicable to image sets with distinctive patterns. The current implementation makes use of a set of Grey Level Co-occurrence Matrix texture features that are generated and selec...

Multi Resolution Fuzzy Segmentation of Satellite Images

Novel Texture Pattern Based Multi-level set Segmentation in Cervical Cancer Image Analysis

Computerized framework development is alternate to the Manual Method (MM) of cervical cancer analysis since MM suffers from the human errors, bulk quantities of Pap smear images, work loading and time complexities. Also, severity level prediction via counting of nucleus leads to incorrect prediction under geometric-based feature extraction. The cell-based segmentation evolved in research studie...

3D Solid Texture Shape Descriptors based on Multi-Resolution Pyramids

3D solid textures are important data for computer graphics applications, and the amount of the data is increasing. When a large number of the 3D solid textures are stored as databases, similarity search techniques and classification techniques are essential for effective use of the databases. We have applied 3D HLAC (Higher Order Local Autocorrelation) masks to 3D solid textures for extracting ...

Set-Permutation-Occurrence Matrix Based Texture Segmentation

We have investigated a combination of statistical modelling and expectation maximisation for a texture based approach to the segmentation of mammographic images. Texture modelling is based on the implicit incorporation of spatial information through the introduction of a set-permutation-occurrence matrix. Statistical modelling is used for data generalisation and noise removal purposes. Expectat...