Extraction of flower regions from complex background is a difficult task, it is an important part of flower image retrieval, and recognition .Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer"s goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. This paper studies the flower image segmentation in complex background. Based on the visual characteristics differences of the flower and the surrounding objects, the flower from different backgrounds are separated into a single set of flower image pixels. The segmentation methodology on flower images consists of five steps. Firstly, the original image of RGB space is transformed into Lab color space. In the second step "a" component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in "achannel" is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, we extract the object by the gray level co-occurrence matrix for texture segmentation. The GLCMs essentially represent the joint probability of occurrence of grey-levels for pixels with a given spatial relationship in a defined region. Finally, the segmentation result is corrected by mathematical morphology methods. The algorithm was tested on plague image database and the results prove to be satisfactory. The algorithm was also tested on medical images for nucleus segmentation.

Texture segmentation has been active area of research for over three decades. Texture segmentation is the method of dividing an image into homogenous regions. Morphology based approaches to texture segmentation have gained popularity in recent years in the general computer vision literature because of their capability to give a globally optimal solution. In this paper, a new morphological approach based on local tetra patterns (LTrPs) is proposed for the segmentation of textures. The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between referenced pixel and its neighbors by computing gray-level difference whereas LTrP encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in horizontal and vertical directions. The algorithm is tested on Brodatz and VisTex databases and the results obtained show good segmentation.