Gloria Díaz

A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images

Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-consuming task. This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects, from which erythrocytes are found. Finally, a two step classification process identifies infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and average specificity of 91.2%.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.

Infected cell identification in thin blood images based on color pixel classification: comparison and analysis

Malaria is an infectious disease which is mainly diagnosed by visual microscopical evaluation of Giemsa-stained thin blood films using a differential analysis of color features. This paper presents the evaluation of a color segmentation technique, based on standard supervised classification algorithms. The whole approach uses a general purpose classifier, which is parameterized and adapted to the problem of separating image pixels into three different classes: parasite, blood red cells and background. Assessment included not only four different supervised classification techniques - KNN, Naive Bayes, SVM and MLP - but different color spaces -RGB, normalized RGB, HSV and YCbCr-. Results show better performance for the KNN classifiers along with an improving feature characterization in the normalized RGB color space.

Micro-structural tissue analysis for automatic histopathological image annotation

This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part‐based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block‐based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts. Microsc. Res. Tech., 2011.

Automatic clump splitting for cell quantification in microscopical images

This paper presents an original method for splitting overlapped cells in microscopical images, based on a template matching strategy. First, a single template cell is estimated using an Expectation Maximization algorithm applied to a collection of correctly segmented cells from the original image. Next, a process based on matching the template against the clumped shape and removing the matched area is applied iteratively. A chain code representation is used for establishing best correlation between these two shapes. Maximal correlation point is used as an landmark point for the registration approach, which finds the affine transformation that maximises the intersection area between both shapes. Evaluation was carried out on 18 images in which 52 clumped shapes were present. The number of found cells was compared with the number of cells counted by an expert and results show agreement on a 93% of the cases.

Automatic BI-RADS description of mammographic masses

This paper presents a CBIR (Content Based Information Retrieval) framework for automatic description of mammographic masses according to the well known BI-RADS lexicon Unlike other approaches, we do not attempt to segment masses but instead, we describe the regions an expert selects, after the series of rules defined in the BI-RADS lexicon The content based retrieval strategy searches similar regions by automatically computing the Mahalanobis distance of feature vectors that describe main shape and texture characteristics of the selected regions A description of a test region is based on the BI-RADS description associated to the retrieved regions The strategy was assessed in a set of 444 masses with different shapes and margins Suggested descriptions were compared with a ground truth already provided by the data base, showing a precision rate of 82.6% for the retrieval task and a sensitivity rate of 80% for the annotation task.

Histopathological image classification using stain component features on a pLSA model

Semantic annotation of microscopical field of views is one of the key problems in computer assistance of histopathological images. In this paper a new method for extracting patch descriptors is proposed and evaluated using a probabilistic latent semantic analysis (pLSA) classification model. The proposed approach is based on the analysis of the different dyes used to stain the histological sample. This analysis allows to find local regions that correspond to cells in the image, which are then described by the SIFT descriptors of the stain components. The proposed approach outperforms the conventional sampling and description strategies, proposed in the literature.

A content-based retrieval of mammographic masses using the curvelet descriptor

Computer-aided diagnosis (CAD) that uses content based image retrieval (CBIR) strategies has became an important research area. This paper presents a retrieval strategy that automatically recovers mammography masses from a virtual repository of mammographies. Unlike other approaches, we do not attempt to segment masses but instead we characterize the regions previously selected by an expert. These regions are firstly curvelet transformed and further characterized by approximating the marginal curvelet subband distribution with a generalized gaussian density (GGD). The content based retrieval strategy searches similar regions in a database using the Kullback-Leibler divergence as the similarity measure between distributions. The effectiveness of the proposed descriptor was assessed by comparing the automatically assigned label with a ground truth available in the DDSM database.1 A total of 380 masses with different shapes, sizes and margins were used for evaluation, resulting in a mean average precision rate of 89.3% and recall rate of 75.2% for the retrieval task.

Multi-view information fusion for automatic BI-RADS description of mammographic masses

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

Visual examination of blood and bone marrow smears is an important tool for diagnosis, prevention and treatment of clinical patients. The interest of computer aided decision has been identified in many medical applications: automatic methods are being explored to detect, classify and measure objects in hematological cytology. This chapter presents a comprehensive review of the state of the art and currently available literature and techniques related to automated analysis of blood smears. The most relevant image processing and machine learning techniques used to develop a fully automated blood smear analysis system which can help to reduce time spent for slide examination are presented. Advances in each component of this system are described in acquisition, segmentation and detection of cell components, feature extraction and selection approaches for describing the objects, and schemes for cell classification. Gloria Díaz National University of Colombia, Colombia