Texture Image Segmentation Approach Based on Neural Networks

Abstract

One of the major problems in texture analysis is segmenting images into different regions based on textures. In this paper, we present a new approach of texture segmentation, which is based on both Kohonen maps and mathematical morphology, using three different texture features, namely, Haralick features based on gray-level co-occurrence matrix (GLCM), fractal features based on fractal dimension using the differential box counting method, and wavelet features based on wavelet transform. These features are used to train the Kohonen Network, which will be represented by the underlying probability density function (PDF). The segmentation of this map’s representation is made by morphological watershed transformation. In the final part of our algorithm, this will help on the segmentation of the textural image, by assigning each pixel to a modal region extracted from the map. Our work covers the results obtained by the three extraction methods taking into consideration the execution time and the error rate.