Maria Trujillo

A measure for accuracy disparity maps evaluation

The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitude of the errors and the inherent error of stereo systems, in regard to the inverse relation between depth and disparity. Consequently, different disparity maps, with quite similar percentages of BMP, may produce 3D reconstructions of largely different qualities. In this paper, a ground-truth based measure of errors in estimated disparity maps is presented. It offers advantages over the BMP, since it takes into account the magnitude of the errors and the inverse relation between depth and disparity. Experimental validations of the proposed measure are conducted by using two state-of-the-art quantitative evaluation methodologies. Obtained results show that the proposed measure is more suited than BMP to evaluate the depth accuracy of the estimated disparity map.

A Method for Reducing the Cardinality of the Pareto Front

Multi-objective problems are characterised by the presence of a set of optimal trade-off solutions –a Pareto front–, from which a solution has to be selected by a decision maker. However, selecting a solution from a Pareto front depends on large quantities of solutions to select from and dimensional complexity due to many involved objectives, among others. Commonly, the selection of a solution is based on preferences specified by a decision maker. Nevertheless a decision maker may have not preferences at all. Thus, an informed decision making process has to be done, which is difficult to achieve. In this paper, selecting a solution from a Pareto front is addressed as a multi-objective problem using two utility functions and operating in the objective space. A quantitative comparison of stereo correspondence algorithms performance is used as an application domain.

An Automatic Segmentation Approach of Epithelial Cells Nuclei

Histology images are used to identify biological structures present in living organisms — cells, tissues, organs, and parts of organs. E-Learning systems can use images to aid teaching how morphological features relate to function and understanding which features are most diagnostic of organs. The structure of cells varies according to the type and function of the cell. Automatic cell segmentation is one of the challenging tasks in histology image processing. This problem has been addressed using morphological gradient, region-based methods and shape-based method approaches, among others. In this paper, automatic segmentation of nuclei of epithelial cells is addressed by including morphological information. Image segmentation is commonly evaluated in isolation. This is either done by observing results, via manual segmentation or via some other goodness measure that does not rely on ground truth images. Expert criteria along with images manually segmented are used to validate automatic segmentation results. Experimental results show that the proposed approach segments epithelial cells in a close way to expert manual segmentations. An average sensitivity of 76% and an average specificity of 77% were obtained on a selected set of images.

Automatic Classification of Coating Epithelial Tissue

Histology images may be used in E-Learning systems to teach how morphological features and function of each organ contribute to its identification. Automatic classification of coating epithelial cells is an open problem in image processing. This problem has been addressed using morphological gradient, region-based and, shape-based method, among others. In this paper, coating epithelial cells are recognised and classified into: Flat, Cubic and Cylindrical. Epithelial cells are classified based on sphericity and projection. Information about sphericity is used to classify cells into cubic and a measure based in projecting cell nucleus into light region is used to classify into flat and cylindrical. Experimental validations are conducted according to expert criteria, along with manually annotated images, as a ground-truth. Experimental results revealed that the proposed approach recognised coating epithelial cells and classified tissues in a similar way to how experts have performed these classifications.

Identifying Loose Connective and Muscle Tissues on Histology Images

Histology images are used to identify biological structures present in living organisms — cells, tissues, and organs — correctly. The structure of tissues varies according to the type and purpose of the tissue. Automatic identification of tissues is an open problem in image processing. In this paper, the identification of loose connective and muscle tissues based on morphological tissue information is presented.

Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM.

BACKGROUND AND OBJECTIVE Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies. METHOD In this paper, we demonstrate that it is possible to automatically classify cardiovascular tissues using texture information and Support Vector Machines (SVM). Additionally, we realised that it is feasible to recognise several cardiovascular organs following the same process. The texture of histological images was described using Local Binary Patterns (LBP), LBP Rotation Invariant (LBPri), Haralick features and different concatenations between them, representing in this way its content. Using a SVM with linear kernel, we selected the more appropriate descriptor that, for this problem, was a concatenation of LBP and LBPri. Due to the small number of the images available, we could not follow an approach based on deep learning, but we selected the classifier who yielded the higher performance by comparing SVM with Random Forest and Linear Discriminant Analysis. Once SVM was selected as the classifier with a higher area under the curve that represents both higher recall and precision, we tuned it evaluating different kernels, finding that a linear SVM allowed us to accurately separate four classes of tissues: (i) cardiac muscle of the heart, (ii) smooth muscle of the muscular artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation was conducted using 3000 blocks of 100 × 100 sized pixels, with 600 blocks per class and the classification was assessed using a 10-fold cross-validation. RESULTS using LBP as the descriptor, concatenated with LBPri and a SVM with linear kernel, the main four classes of tissues were recognised with an AUC higher than 0.98. A polynomial kernel was then used to separate the elastic artery and vein, yielding an AUC in both cases superior to 0.98. CONCLUSION Following the proposed approach, it is possible to separate with very high precision (AUC greater than 0.98) the fundamental tissues of the cardiovascular system along with some organs, such as the heart, arteries and veins.

Automatic recognition of fundamental tissues on histology images of the human cardiovascular system

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nucleis features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissues function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition.

A histological ontology of the human cardiovascular system

AbstractBackgroundIn this paper, we describe a histological ontology of the human cardiovascular system developed in collaboration among histology experts and computer scientists.ResultsThe histological ontology is developed following an existing methodology using Conceptual Models (CMs) and validated using OOPS!, expert evaluation with CMs, and how accurately the ontology can answer the Competency Questions (CQ). It is publicly available at http://bioportal.bioontology.org/ontologies/HO and https://w3id.org/def/System.ConclusionsThe histological ontology is developed to support complex tasks, such as supporting teaching activities, medical practices, and bio-medical research or having natural language interactions.

Tissues Classification of the Cardiovascular System Using Texture Descriptors

In this paper, we present an approach to automatically classify tissues of the cardiovascular system using texture information. Additionally, this process makes possible to identify some cardiovascular organs, since some tissues belong to muscles associated to those, i.e. identifying the tissue makes possible to identify the organ. We have assessed rotation invariant Local Binary Patterns (LBPri) and Haralick features to describe the content of histological images. We also assessed Random Forest (RF) and Linear Discriminant Analysis (LDA) for the classification of these descriptors. The tissues were classified into four classes: (i) cardiac muscle of the heart, (ii) smooth muscle of the elastic artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation is conducted with a set of 2400 blocks of \(100\times 100\) pixels each. The classifier was assessed using a 10-fold cross-validation. The best AUCs (0.9875, 0.9994 and 0.9711 for the cardiac muscle of the heart, the smooth muscle of muscular artery, the smooth muscle of the large vein and the elastic artery classes, respectively) are achieved by LBPri and RF.

Global and Local Features for Char Image Classification

The use of image analysis in understanding how powdered coal burns during the combustion plays a significant role in setting combustion parameters. During the pulverised coal combustion, char particles are produced by devolatising coal and represent the dominant stage in the combustion process. The pyrolysis produces different char morphologies that determine coal reactivity affecting the performance of coal combustion in power plants and the emissions of carbon dioxide, CO2. In this paper, an automatic char classification model is proposed using supervised learning. A general classification model is trained given a set of char particles classified by an expert. In particular, Support Vector Machine (SVM) and Random Forest are the trained classifiers. Two types of features are evaluated to built classification models: local and global. Local features are calculated using the Scale-Invariant Transform Feature (SIFT). Global features are defined based on the morphology classification by the International Committee for Coal and Organic Petrology (ICCP). Each classifier is trained by SVM or Random Forest and evaluated using a 10-fold cross-validation. The 70% of data is used as training set and the rest as testing set. A total of 2928 char-particle images are used for evaluating performance of classification models. Additionally, evaluation of model generalisation capability is done using a test set of 732 char particle images. Results showed that global features – defined by the application domain – increase significantly the accuracy of classifiers. Also, global features have more generalisation power than local features. Local features lack of meaning in the application domain and classifiers build with local features – such as SIFT – depend crucially on the training set.