G. Troglio

Automatic Extraction of Ellipsoidal Features for Planetary Image Registration

With the launch of several planetary missions in the last decade, a large amount of planetary images has been already acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Here, we propose a new unsupervised method for the extraction of different features of elliptical and geometrically compact shapes, such as craters and rocks of compact shape (e.g., boulders), to be used for image registration purposes. This approach is based on the combination of several image processing techniques, including watershed segmentation and the generalized Hough transform. The method potentially has application for extraction of craters, rocks, and other geological features.

Automatic Extraction of Planetary Image Features

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of features from the Lunar surface, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration, and can be generalized to other planetary images as well.

Automatic registration of retina images based on genetic techniques

The aim of this paper is to develop an automatic method for the registration of multitemporal digital images of the fundus of the human retina. The images are acquired from the same patient at different times by a color fundus camera. The proposed approach is based on the application of global optimization techniques to previously extracted maps of curvilinear structures in the images to be registered (such structures being represented by the vessels in the human retina): in particular, a genetic algorithm is used, in order to estimate the optimum transformation between the input and the base image. The algorithm is tested on two different types of data, gray scale and color images, and for both types, images with small changes and with large changes are used. The comparison between the registered images using the implemented method and a manual one points out that the proposed algorithm provides an accurate registration. The convergence to a solution is not possible only when dealing with images taken from very different view-points.

Crater detection based on marked point processes

A novel automatic method is developed for the detection of features in planetary images. Although many automatic feature extraction methods have been proposed for for remote sensing images of the Earth, these methods are typically unfeasible for planetary data that generally present low contrast and uneven illumination characteristics.

Unsupervised change-detection in retinal images by a multiple-classifier approach

The aim of this work is the development of an unsupervised method for the detection of the changes that occurred in multitemporal digital images of the fundus of the human retina, in terms of white and red spots. The images are acquired from the same patient at different times by a fundus camera. The proposed method is an unsupervised multiple classifier approach, based on a minimum-error thresholding technique. This technique is applied to separate the “change” and the “no-change” areas in a suitably defined difference image. In particular, the thresholding approach is applied to selected sub-images: the outputs of the different windows are combined with a majority vote approach, in order to cope with local illumination differences. A quantitative assessment of the change detection performances suggests that the proposed method is able to provide accurate change maps, although possibly affected by misregistration errors or calibration/acquisition artifacts. The comparison between the results obtained using the implemented multiple classifier approach and a standard one points out that the proposed algorithm provides an accurate detection of the temporal changes.

Automatic Change Detection of Retinal Images

The aim of the presented study is the development of an automatic method for change detection in multitemporal digital images of the human retina. The images are acquired from the same patient at different times by a color fundus camera. The method proposed here is based on the preliminary automatic registration of multitemporal images, and the detection of the changes that can occur in the retina during time, by comparing the registered images. In order to achieve the temporal registration of the retinal images, an automatic approach based on global optimization techniques is proposed here. In particular, in order to estimate the optimum transformation between the input and the base image, a genetic algorithm is used to optimize the match between previously extracted maps of curvilinear structures in the images to be registered (such structures being represented by the vessels in the human retina). The proposed approach for the detection of temporal changes within the registered images is based on the application of an unsupervised algorithm, in order to cope with the lack of training information about the statistic of the changed areas in fundus images.

Unsupervised Change Detection in Multitemporal Images of the Human Retina

Diabetes is a growing epidemic in the world, due to population growth, aging, urbanization, and increasing prevalence of obesity and physical inactivity. Diabetic retinopathy is the leading cause of blindness in the western working age population. Early detection can enable timely treatment minimizing further deterioration. Clinical signs observable by digital fundus imagery, include microaneurysms, hemorrhages, and exudates, among others. In this chapter, a new method to help the diagnosis of retinopathy and to be used in automated systems for diabetic retinopathy screening is presented. In particular, the automatic detection of temporal changes in retinal images is addressed. The images are acquired from the same patient during different medical visits by a color fundus camera. The presented method is based on the preliminary automatic registration of multitemporal images, and the detection of the temporal changes in the retina, by comparing the registered images. An automatic registration approach, based on the extraction of the vascular structures in the images to be registered and the optimization of their match, is proposed. Then, in order to achieve the detection of temporal changes, an unsupervised approach, based on a minimum-error thresholding technique, is proposed. The algorithm is tested on color fundus images with small and large changes.