Eva Cernadas

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.

Automatic detection and classification of grains of pollen based on shape and texture

Palynological data are used in a wide range of applications. Some studies describe the benefits of the development of a computer system to pollinic analysis. The system should involve the detection of the pollen grains on a slice, and their classification. This paper presents a system that realizes both tasks. The latter is based on the combination of shape and texture analysis. In relation to shape parameters, different ways to understand the contours are presented. The resulting system is evaluated for the discrimination of species of the Urticaceae family which are quite similar. The performance achieved is 89% of correct pollen grain classification

Recognizing marbling in dry-cured Iberian ham by multiscale analysis

Dry-cured Iberian ham is one of the most valuable meat products in Spain, with a first-rate consumer acceptance. Visually discernible characteristics of fat and lean, such as marbling, have all effect on the acceptability and palatability of ripened Iberian ham pieces. Important marbling properties include the amount and spatial distribution of intramuscular fat streaks. Chemical processing is the only proved way to determine the fat level of pig meat, but this technique is tedious, destroying and unable to offer information about fat distribution. The determination of Iberian ham sensorial quality has traditionally involved appraisal of marbling characteristics by descriptive analysis methods, which rely heavily on visual evaluation and testing by panels of trained graders. We present a novel method to recognize marbling in Iberian ham images to provide the base for the design of an automatic, non-destroying expert computer system, based on computer vision and pattern recognition techniques, which shall allow food technology industries to evaluate and characterize Iberian ham independently of the subjective and variable criteria of human testers.

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.

Comparison of region and edge segmentation approaches to recognize fish oocytes in histological images

The study of biology and population dynamics of fish species requires the estimation of fecundity in individual fish in a routine way in many fisheries laboratories. The traditional procedure used by fisheries research is to count the oocytes manually on a subsample of known weight of the ovary, and to measure few oocytes under a binocular microscope. This process could be done on a computer using an interactive tool to count and measure oocytes. In both cases, the task is very time consuming, which implies that fecundity studies are rarely conducted routinely. This work represents the first attempt to design an automatic algorithm to recognize the oocytes in histological images. Two approaches based on region and edge information are described to segment the image and extract the oocytes. An statistical analysis reveals that higher than 74% of oocytes are recognized for both approaches, when an overlapping area between machine detection and true oocyte demanded is greater than 75%.

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.