Pilar Carrión

Short Note: Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics

Iberian pork comes from genuinely bred Southwest Iberian Peninsula pigs traditionally fattened with acorns and pasture in an extensive production system. Dry-cured loins and hams constitute the main uncooked pork products with high sensorial quality and a first rate consumer acceptance, leading to high prices in the market. Several aspects related to quality in Iberian products have been examined by using chemical and sensorial procedures to provide quality. However, all these approaches are tedious and destroy the item. In addition, food science has shown little interest in MRI to explore meat products in a non-invasive way. Therefore, this paper introduce an objective and non-destructive methodology to classify Iberian loins consistently. It is based on texture analysis of MRI images displaying dry-cured pork loins. A statistical evaluation is provided for a set of 47 loins to predict three levels of different sensorial characteristics.

Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary

The estimation of fecundity and reproductive cells (oocytes) development dynamic is essential for an accurate study of biology and population dynamics of fish species. This estimation can be developed using the stereometric method to analyse histological images of fish ovary. However, this method still requires specialised technicians and much time and effort to make routinary fecundity studies commonly used in fish stock assessment, because the available software does not allow an automatic analysis. The automatic fecundity estimation requires both the classification of cells depending on their stage of development and the measurement of their diameters, based on those cells that are cut through the nucleous within the histological slide. Human experts seem to use colour and texture properties of the image to classify cells, i.e., colour texture analysis from the computer vision point of view. In the current work, we provide an exhaustive statistical evaluation of a very wide variety of parallel and integrative texture analysis strategies, giving a total of 126 different feature vectors. Besides, a selection of 17 classifiers, representative of the currently available classification techniques, was used to classify the cells according to the presence/absence of nucleous and their stage of development. The Support Vector Machine (SVM) achieves the best results for nucleous (99.0% of accuracy using colour Local Binary Patterns (LPB) feature vector, integrative strategy) and for stages of development (99.6% using First Order Statistics and grey level LPB, parallel strategy) with the species Merluccius merluccius, and similar accuracies for Trisopterus luscus. These results provide a high reliability for an automatic fecundity estimation from histological images of fish ovary.

Classification of honeybee pollen using a multiscale texture filtering scheme

Abstract.People are interested in the composition of honeybee pollen due to its nutritional value and therapeutic benefits. Its palynological composition depends on the local flora surrounding the beehive, and its identification is currently done manually using optical microscopy. This procedure is tedious and expensive in systematic application and is unable to automatically separate pollen loads of different species of plants. We present an automatic methodology to discriminate pollen loads based on texture image classification. Texture features are generated using a multiscale filtering scheme. A statistical evaluation of the algorithm is provided and discussed.

Common scab detection on potatoes using an infrared hyperspectral imaging system

The common scab is a skin disease of the potato tubers that decreases the quality of the product and influences significantly the price. We present an objective and non-destructive method to detect the common scab on potato tubers using an experimental hyperspectral imaging system. A supervised pattern recognition experiment has been performed in order to select the best subset of bands and classification algorithm for the problem. Support Vector Machines (SVM) and Random Forest classifiers have been used. We map the amount of common scab in a potato tuber by classifying each pixel in its hyperspectral cube. The result is the percentage of the surface affected by common scab. Our system achieves a 97.1% of accuracy with the SVM classifier.

Non-destructive detection of hollow heart in potatoes using hyperspectral imaging

We present a new method to detect the presence of the hollow heart, an internal disorder of the potato tubers, using hyperspectral imaging technology in the infrared region. A set of 468 hyperspectral cubes of images has been acquired from Agria variety potatoes, that have been cut later to check the presence of a hollow heart. We developed several experiments to recognize hollow heart potatoes using different Artificial Intelligence and Image Processing techniques. The results show that Support Vector Machines (SVM) achieve an accuracy of 89.1% of correct classification. This is an automatic and non-destructive approach, and it could be integrated into other machine vision developments.

Influence of normalization and color space to color texture classification

Abstract Color texture classification has recently attracted significant attention due to its multiple applications. The color texture images depend on the texture surface and its albedo, the illumination, the camera and its viewing position. A key problem to get an acceptable performance is the ambient illumination, which can vary the perceived structures in the surface. Given a color texture classification problem, it would be desirable to know which is the best approach to solve the problem making the minimal assumptions about the illumination conditions. The present work does an exhaustive evaluation of the state-of-the-art color texture classification methods, considering 5 different color spaces, 12 normalization methods to achieve illumination invariances, 19 texture feature vectors and 23 pure color feature vectors. Our experiments allow to conclude that parallel approaches are better than integrative approaches for color texture classification achieving the first positions in the Friedman ranking. Multiresolution Local Binary Patterns (MLBP) are the best intensity texture features, followed by wavelet and Gabor filters combined with luminance–chrominance color spaces (Lab and Lab2000HL), and for pure color classification the best are First Order Statistics (FOS) calculated in RGB color space. For intensity texture features, the learning methods work better on the four smallest datasets, although they could not be tested in other four bigger datasets due to its huge computational cost, nor with color texture classification. Normalization and color spaces slightly increase the average accuracy of color texture classification, although the differences achieved using normalization are not statistically significant in a paired T-Test. Lab2000HL and RGB are the best color spaces, but the former is the slowest one. Regarding elapsed time, the best vector features MLBP for intensity texture, Daub4 (Daubechies filters using mean and variance statistics) for color texture and FOS, for pure color are nearly the fastest or are in the middle interval of all the tested methods.

An Application for Knowledge Discovery Based on a Revision of VPRS Model

In this paper, we present a particular study of the negative factors that affect the performance of university students. The analysis is carried out using the CAI (Conjuntos Aproximados con Incertidumbre) model that is a new revision of the VPRS (Variable Precision Rough Set) model. The major contribution of the CAI model is the approximate equality among knowledge bases. This concept joined with the revision of the process of knowledge reduction (concerning both attributes and categories), allow a significant reduction in the number of generated rules and the number or attributes per rule as it is showed in the case of study.

Determine the Composition of Honeybee Pollen by Texture Classification

Humans are interested in the knowledge of honeybee pollen composition, which depends on the local flora surrounding the beehive, due to their nutritional value and therapeutical benefits. Currently, pollen composition is manually determined by an expert palynologist counting the proportion of pollen types analyzing the pollen of the hive with an optical microscopy. This procedure is tedious and expensive for its systematic application. We present an automatic methodology to discriminate pollen loads of various genus based on texture classification. The method consists of three steps: after selection non-blurred regions of interest (ROIs) in the original image, a texture feature vector for each ROI is calculated, which is used to discriminate between pollen types. An statistical evaluation of the algorithm is provided and discussed.

Statistical and wavelet based texture features for fish oocytes classification

The study of biology and population dynamics of fish species requires the estimation of fecundity parameters in individual fish in many fisheries laboratories. The traditional procedure used in fisheries research is to classify and count the oocytes manually on a subsample of known weight of the ovary, and to measure few oocytes under a binocular microscope. With an adequate interactive tool, this process might be done on a computer. However, in both cases the task is very time consuming, with the obvious consequence that fecundity studies are not conducted routinely. In this work we develop a computer vision system for the classification of oocytes using texture features in histological images. The system is structured in three stages: 1) extraction of the oocyte from the original image; 2) calculation of a texture feature vector for each oocyte; and 3) classification of the oocytes using this feature vector. A statistical evaluation of the proposed system is presented and discussed.

Govocitos: A software tool for estimating fish fecundity based on digital analysis of histological images

Abstract To estimate productivity of a fish stock, the precise determination of fish fecundity is essential. The stereological method accurately estimates fecundity from histological images of a fish gonad. For that purpose, a hexagonal grid is overlaid on the histological image and the number of grid points associated to each oocyte (reproductive cells) category and the number of oocytes in each category is counted. This process is done manually often using off-the-shelf software, but it is very time-consuming, requires specialized technicians, and does not allow to review the calculations. In this paper, we describe and evaluate the software Govocitos, which offers an easy and automatic way to estimate fecundity using the stereological method. Govocitos contains a module to automatically detects the matured oocytes in the slice (nearly 80% of oocytes are correctly detected) and a module to automatically classify the oocytes according to the presence/absence of nucleus (with 84% of accuracy) and to three development stages (with 87% of accuracy). It also provides a user friendly GUI that allows the experts to modify the outlines and classifications of oocytes, to calculate diameters, areas and roundness, to build diameter frequency histograms, to count the points and objects inside the grid, to estimate partial and potential fecundity and to export the data to files and into a database. In addition, Govocitos provides the possibility of varying grid characteristics, it can be trained to work with different species and it allows to check and supervise the calculations whenever needed including in a later point in time. Govocitos is a free software that can be downloaded from http://lia.ei.uvigo.es/daeira/software/govocitos or http://citius.usc.es/w/govocitos .