A. Garrido

High frequency of altered HLA class I phenotypes in invasive breast carcinomas

We studied 105 tumor samples obtained from patients diagnosed as having breast carcinomas for HLA class I and II (DR) antigen expression, using a panel of mAbs defining HLA-monomorphic, locus-specific and allele-specific determinants. Peripheral blood lymphocytes from patients were also typed for HLA alleles. The results indicated total HLA class I losses in 55 patients (52.3%), HLA-A locus losses in four patients (3.8%), HLA-B locus losses in eight patients (7.6%), and A, B, locus losses in 10 patients (9.5%). The remaining 28 patients whose tissues reacted positively with monomorphic- and locus-specific mAbs were tested for HLA allelic losses using several anti-HLA mAbs defining A2, A3, A9, B8, B12, etc. Of these 28 patients, 16 (57%) showed one or more losses of HLA reactivity. These results indicated that in 88.5% of patients we detected a particular HLA-altered tumor phenotype. The downregulation of HLA class I antigens in breast carcinomas may thus be more frequent than previously reported, and patients without HLA class I downregulation may be the exception rather than the rule. It cannot be ruled out that HLA alterations are present in some of the 12 patients with an apparently normal HLA phenotype, as some HLA alleles could not be studied because of the lack of appropriate mAbs. These HLA alterations could represent an important step associated with tumor invasion, conferring to the tumor cells the ability to escape from T-lymphocyte recognition.

Applying deformable templates for cell image segmentation

Abstract This paper presents an automatic method, based on the deformable template approach, for cell image segmentation under severe noise conditions. We define a new methodology, dividing the process into three parts: (1) obtain evidence from the image about the location of the cells; (2) use this evidence to calculate an elliptical approximation of these locations; (3) refine cell boundaries using locally deforming models. We have designed a new algorithm to locate cells and propose an energy function to be used together with a stochastic deformable template model. Experimental results show that this approach for segmenting cell images is both fast and robust, and that this methodology may be used for automatic classification as part of a computer-aided medical decision making technique.

High frequency of altered HLA class I phenotypes in laryngeal carcinomas.

The exact frequency of HLA class I losses in human tumors is unknown. We have previously shown that primary breast and colorectal carcinomas frequently lose HLA class I molecules (88% and 73%, respectively). Now we report that this phenomenon is also a frequent event in laryngeal carcinomas. Of a total of 76 laryngeal carcinomas analyzed, 66% of the tumors showed an alteration in HLA class I phenotype. These altered HLA phenotypes were classified as total HLA loss (10.52%) (phenotype I); HLA-A locus-specific loss (13.15%) (phenotype IIIa); HLA-B locus-specific loss (10.52%) (phenotype IIIb); HLA allelic loss (27.63%) (phenotype IV); and HLA-A and B locus loss (3.9%). Comparison of histopathological parameters with HLA expression showed that poorly differentiated tumors had the lowest levels of HLA class I expression (p < 0.05).

Boundary simplification using a multiscale dominant-point detection algorithm

Abstract In this paper we intend to characterize boundaries using the Scale-space theory. The aim we try to achieve is the description of a boundary in relation to a subset of points—dominant points—that are obtained from a new multiscale representation of the boundary. Dominant points are characterized by a high curvature value (in the original or smoothed boundary). As a result, the boundary is represented using those points as well as an appropriate interpolation method (the linear one in the simplest case) among them. As the basic tool of our work we will introduce a new multiscale dominant point detection algorithm that detects the points at their natural scales through a reliability condition with respect to the original curve. Because we want to apply the algorithms on complex enough boundaries, we use cartographic boundaries (in which several structures can be obtained at different scales) to evaluate the results.

Physically-based active shape models: initialization and optimization

In this paper we describe a new approach for 2-D object segmentation using an automatic method applied on images with problems as partial information, overlapping objects, many objects in a single scene, severe noise conditions and locating objects with a very high degree of deformation. We use a physically-based shape model to obtain a deformable template, which is defined on a canonical orthogonal coordinate system. The proposed methodology works starting from the output of an edge detector, which is processed to automatically obtain an approximation of the shape. The final estimation of the shapes is obtained fitting a deformable template model, which is defined on a learned surface of deformation. Results from biological images are presented.

Using models of feature perception in distortion measure guidance

Abstract In this paper we present three error measures based on feature perception models, in which pixel errors are computed on locations at which humans might perceive features in the reference image. In the first part of this work, the three schemes of feature detection will be discussed and evaluated in terms of their performance for a simple visual signal-processing task. The first model is based on the use of local intensity gradients, the second based on the use of phase congruency in an image, and the third based on the use of local energy maxima for a few active sensors under a multichannel organization of the reference picture. In the second part of this paper, examples are provided of object detection and recognition applications that illustrate the ability of the induced error measures to predict the detectability of objects in natural backgrounds as well as their perceptual capabilities.

A new edge detector integrating scale-spectrum information

This paper presents a new scale space-based method to extract edges in gray level images. The method is based on a novel representation of gray-level shape called the scale-spectrum space. The scale space representation is used to describe an image at different scales. In order to obtain the original image edges, an edge detector is applied to each simplified image on the corresponding scale. At best, some form of compromise among the edges at different scale levels may be sought. To overcome this problem, we present a stability criterion to combine edges obtained at different scales. Usual problems in edge detection such as displacement, redundancy and error are analyzed and solved using a realistic estimation of displacement of points across scale space. The proposed approach suppress the finer details without weakening or dislocating the larger scale edges (the usual problems of edge detection using an isotropic diffusion procedure) in an improved manner compared to anisotropic diffusion procedures because a tuning function is not required. The proposed methodology is biologically inspired by the behavior of visual cortex neurones as well as retinal cells.

A scale-vector approach for edge detection

This paper presents a method for selecting proper scale parameters for edge detection. To this aim the image is preliminarily segmented into regions with a homogeneous intensity change model, by using a clustering algorithm based on a dissimilarity measure applied on feature vectors associated to the regions. The used algorithm is a standard clustering method, with a reasonable criterion to establish the number of clusters. Once the image has been properly segmented, in each region the most proper scale parameter is determined and applied for edge detection.

COMPUTATIONAL VISUAL DISTINCTNESS METRIC

A new computational visual distinctness metric based on principles of the early human visual system is presented. The metric is applied to quantify (1) the visual distinctness of targets in complex natural scenes and (2) the perceptual differences between compressed and uncompressed images. The new metric is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, (2) to correlate with visual target distinctness estimated by human observers, and (3) to correlate with the quality factor of Joint Photographic Experts Group (JPEG) compressed imagery.

Using optical flow as evidence for probabilistic tracking

In this paper, we presen t an observation model based on the Lucas and Kanade algorithm for computing optical flow, to trac k objects using particle filter algorithms. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques lack the necessary precision. In view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, this model has been used as a natural means of incorporating flow information into the trac king.