## 4 Conclusions  
In recent years, the need to automate the analysis of IIF HEp-2 specimens has been established, in order to obtain a fundamental tool for the diagnosis of autoimmune diseases, and to avoid the subjectivity of human interpretation. For this purpose, in this document a complete and fully automatic CAD system has been proposed, which is able to classify the IIF images, in terms of fluorescent intensity and fluorescent pattern. To this end, our system combines the following steps:  
1. Fluorescence intensity classification: This phase performs a categorization of the fluorescent intensity into positive/negative classes. The goal has been achieved by performing a preprocessing phase of the image, extracting a considerable number of features and implementing an SVM classifier. To achieve a reduction in complexity and an appropriate selection of features, the LDA method was used. 2. Cell segmentation: This phase of the system decomposes the input image, looking for the cells contained in it, without any a-priori knowledge about its intensity level or pattern. In order to address this problem, a method consisting of three steps has been developed--pre-segmentation, initialization by means of a randomized Hough transform, and an active contours model. The average Dice index obtained on the ninety-five images analyzed, was equal to 81.1%. In spite of the remarkable diversity of the patterns analyzed, the method achieves very similar segmentation results for the different patterns, demonstrating a good robustness of the proposed method. 3. Pattern Classification: This phase receives the individual HEp-2 cells from the cell segmentation phase and categorizes them into a set of fluorescent patterns. For this purpose, regarding the staining patterns classification, the CAD system presented here, adopts a differentiated analysis method for each pattern. Starting from a set of preprocessing functions, all possible couplings, in terms of class accuracy, were evaluated, and the best performing combination for each pattern was chosen. For each class under analysis, a large number of features was extracted and a feature reduction phase, based on linear discriminant analysis (LDA), was performed with the aim of selecting the characteristics able to characterize the specific staining pattern. A classification approach based on the one-against-all (OAA) scheme has been implemented; six Support Vector Machine (SVM) classifiers has been implemented to classify the IIF images. The final decision-making process for the cell-staining pattern association is achieved by using a KNN classifier, having six inputs (the six outputs of SVM classifiers). The image classification was obtained by evaluating the pattern rates at the cell-level. The developed system was evaluated on the AIDA public database, consisting of 2080 IIF images, obtaining an overall accuracy of fluorescent intensity and pattern classification, respectively, around 87% and 71%. It has also been evaluated on the public database I3A, obtaining a 75.1% of fluorescent intensity accuracy and 78.6% of pattern classification accuracy. All results were evaluated by comparing them with some of the most representative state-of-the-art work. Unlike most of the other work in the recent literature, our system automatically analyzes all of the main steps of the ANA image analysis. The results obtained from the public database demonstrate that the system can characterize the HEp-2 specimen, in terms of intensity and fluorescent patterns, with better accuracy or is comparable with the most advanced techniques, and is proposed as a valid solution to the problem of ANA test automation. Regarding the future developments of the system, since some patterns have demonstrated a greater difficulty in segmentation (such as nucleolar, nuclear dots, and nuclear membranes), and since it is known that the classification phase is affected by the previous phases, specific processes will be implemented to make improvements.