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Breast cancer is the most common form of cancer among women. The diagnosis is usually performed by the pathologist, that subjectively evaluates tissue samples. The aim of our research is to develop techniques for the automatic classification of cancerous tissue, by analyzing histological samples of intact tissue taken with a biopsy. In our study, we considered 200 images presenting four different conditions: normal tissue, fibroadenosis, ductal cancer and lobular cancer. Methods to extract features have been investigated and described. One method is based on granulometries, which are size-shape descriptors widely used in mathematical morphology. Applications of granulometries lead to distribution functions whose moments are used as features. A second method is based on fractal geometry, that seems very suitable to quantify biological structures. The fractal dimension of binary images has been computed using the euclidean distance mapping. Image classification has then been performed using the extracted features as input of a back-propagation neural network. A new method that combines genetic algorithms and morphological filters has been also investigated. In this case, the classification is based on a correlation measure. Very encouraging results have been obtained with pilot experiments using a small subset of images as training set. Experimental results indicate the effectiveness of the proposed methods. Cancerous tissue was correctly classified in 92.5% of the cases.

Skeletal age assessment is a frequently performed procedure which requires high expertise and a considerable amount of time. Several methods are being developed to assist radiologists in this task by automating the various steps of the process. In this work we describe a method to perform the segmentation step, by means of a modified active contour approach. A set of separate active contours models each bone in a portion of the radiogram. Due to the complexity of the contour, and to the presence of multiple adjacent contours, we add to the commonly used energy termas a first-order derivative energy which allow to take into account the direction of the contour. Moreover, anatomical relationships among bones are modeled as additional internal elastic forces which couple together the contours. Contour energy is optimized using a genetic algorithm. Chromosomes are used to encode positions of snake points, using a polar representation. The genetic optimization overcomes the difficulties related to local minima and to the initialization criterium, and conveniently allows the addition of new energy terms. Experimental results show the method allows to achieve an accurate segmentation of the bone complexes in the region of interest.

Intramuscular fat content in meat influences some important meat quality characteristics. The aim of the present study was to develop and apply image processing techniques to quantify intramuscular fat content in beefs together with the visual appearance of fat in meat (marbling). Color images of M. longissimus dorsi meat samples with a variability of intramuscular fat content and marbling were captured. Image analysis software was specially developed for the interpretation of these images. In particular, a segmentation algorithm (i.e. classification of different substances: fat, muscle and connective tissue) was optimized in order to obtain a proper classification and perform subsequent analysis. Segmentation of muscle from fat was achieved based on their characteristics in the 3D color space, and on the intrinsic fuzzy nature of these structures. The method is fully automatic and it combines a fuzzy clustering algorithm, the Fuzzy c-Means Algorithm, with a Genetic Algorithm. The percentages of various colors (i.e. substances) within the sample are then determined; the number, size distribution, and spatial distributions of the extracted fat flecks are measured. Measurements are correlated with chemical and sensory properties. Results so far show that advanced image analysis is useful for quantify the visual appearance of meat.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper we present an application to food science of image processing technique. We describe a method for determining fat content in beef meat. The industry of meat faces a permanent need for improved methods for meat quality evaluation. Researchers want improved techniques to deepen their understanding of meat features. Expectations of consumers for meat quality grow constantly, which induces the necessity of quality control. Recent advances in the area of computer and video processing have created new ways to monitor quality in the food industry. We investigate the use of a new technology to control the quality of food: NMR imaging. The inherent advantages of NMR images are many. Chief among these unprecedented contrasts between the various structures present in meat like muscle, fat, and connective tissue. Moreover, the three-dimensional nature of the NMR method allow us to analyze isolated cross-sectional slices of the meat and to measure the volumetric content of fat, not only the fat visible on the surface. We propose a segmentation algorithm for the detection of fat together with a filtering technique to remove intensity inhomogeneities in NMR images caused by non-uniformities of the magnetic field during acquisition. Measurements have been successfully correlated with chemical analysis and digital photography. Results show that the NMR technique is a promising non-invasive method to determine the fat content in meat.

In this paper an approach is described for segmenting medical images. We use active contour model, also known as snakes, and we propose an energy minimization procedure based on Genetic Algorithms (GA). The widely recognized power of deformable models stems from their ability to segment anatomic structures by exploiting constraints derived from the image data together with a priori knowledge about the location, size, and shape of these structures. The application of snakes to extract region of interest is, however, not without limitations. As is well known, there may be a number of problems associated with this approach such as initialization, existence of multiple minima, and the selection of elasticity parameters. We propose the use of GA to overcome these limits. GAs offer a global search procedure that has shown its robustness in many tasks, and they are not limited by restrictive assumptions as derivatives of the goal function. GAs operate on a coding of the parameters (the positions and the total number of snake points) and their fitness function is the total snake energy. We employ a modified version of the image energy which consider both the magnitude and the direction of the gradient and the Laplacian of Gaussian. Experimental results on synthetic images as well as on medical images are reported. Images used in this work are ocular fundus images, snakes result very useful in the segmentation of the Foveal Avascular Zone. The experiments performed with ocular fundus images show that the proposed method is promising in the early detection of the diabetic retinopathy.

In this work a computational approach for detecting and quantifying diabetic retinopathy is proposed. Particular attention has been paid to the study of Foveal Avascular Zone (FAZ). In fact, retinal capillary occlusion produces a FAZ enlargement. Moreover, the FAZ is characterized by qualitative changes showing an irregular contour with notchings and indentations. Our study is mainly focused on the analysis of the FAZ and on the extraction of a proper set of features to quantify FAZ alterations in diabetic patients. We propose an automatic segmentation procedure to correctly identify the FAZ boundary. The method was derived from the theory of active contours, also known as snakes, along with genetic optimization. Then we tried to extract features which can capture not only the size of the object, but also its shape and spatial orientation. The theory of moments provides an interesting and useful way for representing the shape of objects. We used a set of region and boundary moments to obtain a FAZ description which is complete enough for diagnostic purposes and in order to assess the effectiveness of moment descriptors we performed several classification experiments to discriminate diabetic from non-diabetic subjects. We used a neural network-based classifier, optimized for the problem, which is able to perform feature selection at the same time as learning, in order to extract a subset of features. The theory of moments provided us with an interesting and useful tool for representing the shape characteristics. In this way we were able to transform the qualitative description of the FAZ used by ophthalmologists into quantitative measurements.

In this paper a method for noise reduction in ocular fundus image sequences is described. The eye is the only part of the human body where the capillary network can be observed along with the arterial and venous circulation using a non invasive technique. The study of the retinal vessels is very important both for the study of the local pathology (retinal disease) and for the large amount of information it offers on systematic haemodynamics, such as hypertension, arteriosclerosis, and diabetes. In this paper a method for image integration of ocular fundus image sequences is described. The procedure can be divided in two step: registration and fusion. First we describe an automatic alignment algorithm for registration of ocular fundus images. In order to enhance vessel structures, we used a spatially oriented bank of filters designed to match the properties of the objects of interest. To evaluate interframe misalignment we adopted a fast cross-correlation algorithm. The performances of the alignment method have been estimated by simulating shifts between image pairs and by using a cross-validation approach. Then we propose a temporal integration technique of image sequences so as to compute enhanced pictures of the overall capillary network. Image registration is combined with image enhancement by fusing subsequent frames of a same region. To evaluate the attainable results, the signal-to-noise ratio was estimated before and after integration. Experimental results on synthetic images of vessel-like structures with different kind of Gaussian additive noise as well as on real fundus images are reported.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper an approach is described for segmenting medical images. We use active contour model, also known as snakes, and we propose an energy minimization procedure based on Genetic Algorithms (GA). The widely recognized power of deformable models stems from their ability to segment anatomic structures by exploiting constraints derived from the image data together with a priori knowledge about the location, size, and shape of these structures. The application of snakes to extract region of interest is, however, not without limitations. As is well known, there may be a number of problems associated with this approach such as initialization, existence of multiple minima, and the selection of elasticity parameters. We propose the use of GA to overcome these limits. GAs offer a global search procedure that has shown its robustness in many tasks, and they are not limited by restrictive assumptions as derivatives of the goal function. GAs operate on a coding of the parameters (the positions of the snake) and their fitness function is the total snake energy. We employ a modified version of the image energy which consider both the magnitude and the direction of the gradient and the Laplacian of Gaussian. Experimental results on medical images are reported. Images used in this work are ocular fundus images, snakes result very useful in the segmentation of the Foveal Avascular Zone. The experiments performed with ocular fundus images show that the proposed method is promising in the early detection of the diabetic retinopathy.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.