Primo Zingaretti

Retina vascular network recognition

The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.

Image sequence recognition

Image sequence recognition is an interesting problem involved in various situations of the Computer Vision field and in particular in mobile robot vision. Typical for this purpose is the motion estimation from a series of frames. Many techniques of motion estimation are described in literature 10, 13, 22 The approaches are normally divided in two categories: pixel based methods and feature based methods. In both the motion is estimated in two steps: 1) 2D motion analysis (feature based) or estimation (pixel based), 2) 3D motion estimation. The pixel based, or flow based, method uses local changes in light intensity to compute optical flow at each image point and then derives 3D motion parameters . The feature based method, on which it falls our choice, firstly extracts the features (as corners, point of curvature, lines, etc.). They are used as features: sharp changes in curvature 15, global properties of moving objects 18, lines and curves 16, centroids 6 Secondly it establishes the correspondences of these features between two successive frames (correspondence problem), and finally it computes motion parameters and object structure from correspondences (structure from motion problem). The motion correspondence is the most difficult problem. Occlusion masks the features and noise creates difficulties. Given n frames taken at different time instants and m points in each frame, the motion correspondence maps a point in one frame to another point in the next frame such that no two points map on the same point. The combinatorial explosiveness of the problem has to be constrained; in Rangarajan and Sah 19 it is proposed the proximal uniformity constraint: given a location of a point in a frame, its location in the next frame lies in the proximity of its previous location. Even tough the problem has not yet been solved, many solutions are proposed for 3D motion estimation, assuming that the correspondences has been established 12, 13, 24 Regularization theory has also been proposed for the numerical improvement of the solution of both feature based and pixel based problems . From the human stand point a vision system may be viewed as performing the following tasks in sequence: detection, tracking and recognition. The detection rises at cortex level; then it follows the tracking of objects contemporary attempting to recognize them. From the machine stand point the movement detection phase may be viewed as a useful mean to focus the system attention so reducing the search space of the recognition algorithms. Particular attention has to be reserved in detecting moving objects in presence of moving background, from monocular image sequence. Several researchers have faced the problem 14, 21 When we take the images from a moving vehicle (for instance with translational movement) it is necessary to distinguish between real and apparent movement. The stationary objects of the scene appear to move along paths radiating from the point toward which we are moving (focus of expansion). By operating a transformation on the image, called Complex Logarithmic Mapping (see Frazier-Nevatia 8), it is possible to convert the problem from one of detecting motion along both the X and Y axes to one of detecting motion from along an angular axis. After executing an horizontal edge detection, if we observe the motion of edges in the vertical direction we can conclude that there is a moving object in the scene. Our approach is feature based and a series of considerations are necessary to understand the solution adopted. We regard as features edges, corners or whole regions. The choice of a feature depends on the facility of retrieving it in the successive frames, forming a correspondence chain. The corner detection may be based on revealing the sharply direction change of intensity gradient. In Rangarajan et al. 20 it is described the construction of a set of operators to detect corners. Being the corners the mainly used features particular attention has been devoted to correspondences among points. For these they may be adopted two approaches: 1) with matching, in which two point patterns, from two consecutive images, are matched (elastic matching 25); 2) without matching, by using the criteria of proximity and regularity of point trajectories. Our approach uses two types of matching: geometric and relational. The geometric matching uses parametrized geometric models and may be viewed as a parametrized optimization problem. The relational matching uses relational representations and may be viewed as the problem of detecting the isomorphism among graphs.

Quantitative analysis of retinal changes in hypertension

Arterial hypertension is a high prevalence disease in Western countries and it is associated with increased risk for cardiovascular accidents. Retinal vessel changes are common findings in patients suffering from long-standing hypertensive disease. Morphological evaluations of the fundus oculi represent a fundamental tool for the clinical approach to the patient with hypertension. A qualitative analysis of the retinal lesions is usually performed and this implies severe limitations both in the classification of the different degrees of the pathology and in the follow-up of the disease. A diagnostic system based on a quantitative analysis of the retinal changes could overcome these problems. Our computerized approach was intended for this scope. The paper concentrates on the results and the implications of a computerized approach to the automatic extraction of numerical indexes describing morphological details of the fundus oculi. A previously developed image processing and recognition system, documented elsewhere and briefly described here, was successfully tested in pre-clinical experiments and applied in the evaluation of normal as well as of pathological fundus. The software system was developed to extract indexes such as caliber and path of vessels, local tortuosity of arteries and arterioles, positions and angles of crossings between two vessels. The reliability of the results, justified by their low variability, makes feasible the standardization of quantitative parameters to be used both in the diagnosis and in the prognosis of hypertension, and also allows prospective studies based upon them.

Segmentation-suggested geometric scheme

The paper presents a geometric scheme suggested by the segmentation process on digitized images. The scheme constitutes a general intermediate representation satisfying both the descriptive and the procedural adequacy. It is constituted by: general primitives which allow the basic shape description; the k-pendulum description model which allows to describe all polygonal shapes at any generality degree; a set of operators which act on polygons transforming the image.

Decision support system for capillaroscopic images

The aim of the paper is to describe a decision support system operating in the area of capillaroscopic images. The system automatically sites the capillaroscopic analyzed image into one of the following classes: normal, diabetic and sclerodermic. The automatic morphometric analysis attempts to imitate the physician behavior and requires the introduction of some particular features connected with the specific domain. These features allow a symbolic representation of the capillary partitioning it into three components: apex, arteriolar, and venular. Each component is qualified by specific attributes which allow the necessary shape evaluations in order to discriminate among the classes of capillaries. The system is hierarchically organized in two levels. The first level is concerned with the segmentation after a noise reduction and an enhancement of the digitized image. This level uses a shell, developed and successfully experimented for many heterogeneous classes of images. The second level is concerned with the effective classification of the previously processed image. It matches the visual data with a model constituted by a semantic network which embeds the geometric and structural a-priori knowledge of all kinds of capillaries. The system has been successfully used in experiments to obtain images of nailfold capillaries of the human finger.

Model attraction in medical image object recognition

This paper presents as new approach to image recognition based on a general attraction principle. A cognitive recognition is governed by a focus on attention process that concentrates on the visual data subset of task- relevant type only. Our model-based approach combines it with another process, focus on attraction, which concentrates on the transformations of visual data having relevance for the matching. The recognition process is characterized by an intentional evolution of the visual data. This chain of image transformations is viewed as driven by an attraction field that attempts to reduce the distance between the image-point and the model-point in the feature space. The field sources are determined during a learning phase, by supplying the system with a training set. The paper describes a medical interpretation case in the feature space, concerning human skin lesions. The samples of the training set, supplied by the dermatologists, allow the system to learn models of lesions in terms of features such as hue factor, asymmetry factor, and asperity factor. The comparison of the visual data with the model derives the trend of image transformations, allowing a better definition of the given image and its classification. The algorithms are implemented in C language on a PC equipped with Matrox Image Series IM-1280 acquisition and processing boards. The work is now in progress.

Attraction based recognition

We introduce, in a model driven recognition approach, the principle of attraction which influences all transformations that may be done, departing from the first image, in order to reach the recognition of objects in a scene. The representation of knowledge has to be reduced to a representation in the feature space. This paper attempts to consider the behaviour of image transformation, for recognition purpose, in this vector space. After the definition of the attraction principle, we describe the attraction process, justify the image chaining concept, and finally interpret a series of processes, including the segmentation refinement, search space reduction and perceptual organization, in terms of the attraction principle.<<ETX>>

Handwritten character recognition using background analysis

The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the `acquisition module, the `binarization module, and the `core module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and `profile analysis, `cut analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as `profiles and `cuts; a `profile is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a `cut is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the `profiles and `cuts, and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 X 32 pixel character is of about 97% after the first iteration, and of over 98% after the second iteration.

Robust approach to ocular fundus image analysis

The analysis of morphological and structural modifications of retinal blood vessels plays an important role both to establish the presence of some systemic diseases as hypertension and diabetes and to study their course. The paper describes a robust set of techniques developed to quantitatively evaluate morphometric aspects of the ocular fundus vascular and micro vascular network. They are defined: (1) the concept of Local Direction of a vessel (LD); (2) a special form of edge detection, named Signed Edge Detection (SED), which uses LD to choose the convolution kernel in the edge detection process and is able to distinguish between the left or the right vessel edge; (3) an iterative tracking (IT) method. The developed techniques use intensively both LD and SED in: (a) the automatic detection of number, position and size of blood vessels departing from the optical papilla; (b) the tracking of body and edges of the vessels; (c) the recognition of vessel branches and crossings; (d) the extraction of a set of features as blood vessel length and average diameter, arteries and arterioles tortuosity, crossing position and angle between two vessels. The algorithms, implemented in C language, have an execution time depending on the complexity of the currently processed vascular network.