Image Segmentation And Feature Extraction Of Algal Cell Based On Multiscale Morphological Image Analysis

Algae is also called phytoplankton. In order to make use of Algae resources and to monitor marine ecosystem in time, identification and classification of Algae is an essential work. For the sake of identifying Algae appearing frequently in China's sea areas efficiently and accurately without professional biologist, research on Automatic Identification and Classification of Algae by cell microscopic image (AICA) is carried out, which take the advantages of digital image processing technique and pattern classification method. Image segmentation and feature extraction play an important role in AICA system, which is the main objective of research in this paper.During recent years, Morphological image analysis is considered to be an important and significant technique for lots of image applications, due to its mature and strict mathematical theory, efficient arithmetic and easiness of hardware implementation. It has become a popular tool for real-time image processing. Therefore, it is not surprising that Morphological image analysis is adopted widely to accomplish the task of image segmentation and some feature extraction in most of the advanced research on automatic identification of Algae by microscopic image around the world. But until now, the developed work about automatic identification of Algae is limited in a few species of algae reported in the literature. Much work should be done to advance the research further.Multiscale analysis is an excellent image analysis method similar to human's vision, with the benefits of representing and processing image in different scale. Thus distinct properties relevant to its scale can be utilized to image segmentation and feature extraction, in particular for those applications where such task is difficult to realize by single scale image analysis. Accordingly, the combination of Multiscale analysis and mathematical morphology provide us with a powerful tool for nonlinear image processing, which is named as Multiscale Morphological image analysis and is applied to solve the problem in process of image segmentation and feature extraction of Algae cell microscopic image.The systemic theory of Multiscale Morphological image analysis is summarized. Based on the systemic theory about Multiscale Morphological image analysis and its up to date development, such as morphological pyramid transformation and morphological wavelet, some innovation is brought out in the field. The main work of this paper is concluded as followed:1 Algae cell microscopic image have the properties of low contrast, weak edge and mixed with lots of noise, which make it very difficult to separate the cell from the background. In addition, according to the variety of Algae, they have multiplicate and abnormal shapes, which add much to the difficulty of image segmentation. In fact, such kind of image segmentation is a challenging work around the world. So it is critical to enhance the edge and reduce noise for the purpose of perfect image segmentation. But contradictions arise in image enhancement and noise magnification, noise reduction and detail reservation. Thus morphological adjunction pyramid transformation and connected morphological transformation are explored to solve the problem:(1) A multiscale edge enhancement approach is proposed based on Haar morphological pyramid transformation, image enhancement by direct grey-level mapping, gradient edge detector and improved soft threshold filtering, aiming at enhance the edge of image while not magnifying noise.The multiscale edge enhancement algorithm mainly take the advantage of non-linearity inherent in morphological adjunction pyramid transformation, which make it possible that the residual images at different resolution decomposed by morphological adjunction pyramid transformation preserves the geometric structure perfectly. Image enhancement by direct grey-level mapping enlarges the dynamic range of the grey-level of the whole image, which enhance the edge and nosie all together. At the moment, gradient edge detector and improved soft threshold filtering are used to trace and enhance the edge only in the residual images. Accordingly, the reconstructed image has the characteristic of enhanced edge without introducing new noise. Experimental results show that this algorithm can enhance the edge of Algae cell while not enlarging noise in the image.(2) An adaptive area open algorithm is built based on connected morphological transformation. The adaptive area open algorithm, banding with morphological reconstruction and attribute thinning, achieve the goal of eliminating noise while preserving the majority of detail information of Algae cell.2 An extremum reservation adaptive morphological wavelet is put forward based on lift wavelet scheme, which performs median filtering to smooth region while leave extremum untouched during decomposition procedure. And it is proved that complete reconstruction can be fulfilled without storage of criterion at each pixel location produced in the procedure of decomposition which saves the memory of computer. Another interest of this adaptive morphological wavelet is that it is non-seperable wavelet transform in spatial domain and has simple architecture for decomposition and reconstruction.3 A multiscale texture feature description method is developed by means of combination of extremum reservation adaptive morphological wavelet with grey-level co-occurrence matrix (GLCM) based statistics approach.This texture feature description method has the virtue of computational efficiency and less sensitivity to noise compared to GLCM based statistics approach itself, so it can describe the texture feature reliably and effectively. Experiments are carried out on identification of 17 species of coscinodiscaceae collected in image database of phytoplankton appearing frequently in China's sea by texture feature. Experimental results indicate that this multiscale texture feature description method precede about 7 percentage in average correct recognition rate compared with the GLCM method.4 An edge reservation adaptive morphological wavelet is established in pursuit of feature extraction. Adaptivity is successfully introduced into the update lifting by taking into account local characteristic such as edge, which enable the edge remain untouched or sharpened at different resolution image decomposed by this adaptive morphological wavelet. At the same time, smoothing effect is gained by median filtering at other region.The edge reservation adaptive morphological wavelet is used to deal with the tough task for extraction of faint girdle or sulcus of dinoflagellates. Experiments demonstrate that contrast of girdle or sulcus of dinoflagellates to the whole image are improved in multiscale decomposition image by this adaptive morphological wavelet, compared to its original grey-level image. Thus it is more favorable for the extraction of girdle or sulcus.