A preliminary study on automated freshwater algae recognition and classification system / Hayat Mansoor Abdullah - Mansoor Abdullah, Hayat (2012)
Freshwater algae can be used as indicators to monitor freshwater ecosystem condition because algae react quickly and predictably to a broad range of pollutants. Research reported that Algae can provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique with artificial neural network (ANN) approaches to automatically detect, recognize, and identify algae genera from the divisions of Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Based on literature review, automated identification of tropical freshwater algae is even non-existent yet, and this study is designed to fill this gap.The development process of the automated freshwater algae detection system involves with many techniques and computer methods such as image preprocessing,segmentation, feature extraction, and classification process by using ANN. Several image preprocessing steps was designed to contrast the images, remove the noise, and improve image quality and overall appearance. Then, Image segmentation applied by using canny edge detection algorithm with specific morphological operation to isolate the image objects components. Image segmentation was divided each input images into sub images where each sub images includes one object only. Feature extraction process was applied to extract some shape and texture features of algae image such as shape index, area, perimeter, minor and major axes, entropy, and Fourier spectrum. Then principal component analysis (PCA) was applied to normalize the extracted features.Novel techniques of auto-alignments with shape index procedures was developed here,where auto-alignments function was used to aligned image objects with horizontal coordinates to extracted object features in similar position. Shape index techniques are also considered a novel techniques developed to assist system in classification of algae based on their biological metrics and taxonomy. Shape index function is an index number of different shape of algae as one component feature of algae diversity. Finally,41of geometrical, texture, and novel features were normalized to feed into artificial neural network (ANN) for classification and recognition purposes. The Feed-forward multilayer perceptron network with back propagation error algorithm (MLP) initialized, and trained with extracted database feature of selected algae image samples. Experiment for comparison between manual process identification by experts with automated recognition process performed by system. The Proposed system was automatically able to classify five kinds of freshwater algae successfully, and experimental results showed that our approach is workable, and had a great accuracy results with more than 93%.Results also indicated that our approach is faster in execution, efficient in recognition rate, and easier for using and implementation if compared with similar developed systems.This study demonstrated application of automated algae recognition of five genera of freshwater algae, there are Navicula form Bacillariophyta, Scenedesmus and Chroococcus from the Chlorophyta division, Microcystis and Oscillatoria from the Cyanobacteria division. The results indicated that MLP is sufficient, and optimal enough to be used for classification of the selected freshwater algae. The accurate results was obtained due to the specific preparation for algae image, well segmentation approach, and the novel methods of auto-alignments and shape index techniques which extremely enhanced system classifier of algae. However, for further improvements, we recommended to be included more features with different ANN such as support vector machine (SVM) and radial basis function (RBF) for better recognition rate as the number of algae species studied increases.