Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright–Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.
Convolutional neural networks (CNNs) have become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of the learned behavior from the underlying task of interest. This lack of transparency is a serious drawback, particularly in applications involving medical screening and diagnosis since poorly understood model behavior could adversely impact subsequent clinical decision-making. Recently, researchers have begun working on this issue and several methods have been proposed to visualize and understand the behavior of these models. We highlight the advantages offered through visualizing and understanding the weights, saliencies, class activation maps, and region of interest localizations in customized CNNs applied to the challenge of classifying parasitized and uninfected cells to aid in malaria screening. We provide an explanation for the models’ classification decisions. We characterize, evaluate, and statistically validate the performance of different customized CNNs keeping every training subject’s data separate from the validation set.
Chest radiography (CXR) has been used as an effective tool for screening tuberculosis (TB). Because of the lack of radiological expertise in resource-constrained regions, automatic analysis of CXR is appealing as a "first reader". In addition to screening the CXR for disease, it is critical to highlight locations of the disease in abnormal CXRs. In this paper, we focus on the task of locating TB in CXRs which is more challenging due to the intrinsic difficulty of locating the abnormality. The method is based on applying a convolutional neural network (CNN) to classify the superpixels generated from the lung area. Specifically, it consists of four major components: lung ROI extraction, superpixel segmentation, multi-scale patch generation/labeling, and patch classification. The TB regions are located by identifying those superpixels whose corresponding patches are classified as abnormal by the CNN. The method is tested on a publicly available TB CXR dataset which contains 336 TB images showing various manifestations of TB. The TB regions in the images were marked by radiologists. To evaluate the method, the images are split into training, validation, and test sets with all the manifestations being represented in each set. The performance is evaluated at both the patch level and image level. The classification accuracy on the patch test set is 72.8% and the average Dice index for the test images is 0.67. The factors that may contribute to misclassification are discussed and directions for future work are addressed.
Automated image analysis of slides of thin blood smears can assist with early diagnosis of many diseases. Automated detection and segmentation of red blood cells (RBCs) are prerequisites for any subsequent quantitative highthroughput screening analysis since the manual characterization of the cells is a time-consuming and error-prone task. Overlapping cell regions introduce considerable challenges to detection and segmentation techniques. We propose a novel algorithm that can successfully detect and segment overlapping cells in microscopic images of stained thin blood smears. The algorithm consists of three steps. In the first step, the input image is binarized to obtain the binary mask of the image. The second step accomplishes a reliable cell center localization that utilizes adaptive meanshift clustering. We employ a novel technique to choose an appropriate bandwidth for the meanshift algorithm. In the third step, the cell segmentation purpose is fulfilled by estimating the boundary of each cell through employing a Gradient Vector Flow (GVF) driven snake algorithm. We compare the experimental results of our methodology with the state-of-the-art and evaluate the performance of the cell segmentation results with those produced manually. The method is systematically tested on a dataset acquired at the Chittagong Medical College Hospital in Bangladesh. The overall evaluation of the proposed cell segmentation method based on a one-to-one cell matching on the aforementioned dataset resulted in 98% precision, 93% recall, and 95% F1-score index.
Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening
for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is
critical for population screening, especially in medical resource constrained developing regions. In this article,
we describe steps that improve previously reported performance of NLM’s CXR screening algorithms and help
advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary
classification systems are combined. The local classifier focuses on subtle and partial presentation of the
disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition,
the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for
the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where
the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated
our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve,
sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global
fusion method over any single classifier.
This study proposes a novel automated method for cardiomegaly detection in chest X-rays (CXRs). The algo- rithm has two main stages: i) heart and lung region localization on CXRs, and ii) radiographic index extraction from the heart and lung boundaries. We employed a lung detection algorithm and extended it to automatically compute the heart boundaries. The typical models of heart and lung regions are learned using a public CXR dataset with boundary markings. The method estimates the location of these regions in candidate ('patient') CXR images by registering models to the patient CXR. For the radiographic index computation, we implemented the traditional and recently published indexes in the literature. The method is tested on a database with 250 abnormal, and 250 normal CXRs. The radiographic indexes are combined through a classifier, and the method successfully classifies the patients with cardiomegaly with a 0:77 accuracy, 0:77 sensitivity and 0:76 specificity.
Tuberculosis (TB) is a major public health problem worldwide, and highly prevalent in developing countries. According to the World Health Organization (WHO), over 95% of TB deaths occur in low- and middle- income countries that often have under-resourced health care systems. In an effort to aid population screening in such resource challenged settings, the U.S. National Library of Medicine has developed a chest X-ray (CXR) screening system that provides a pre-decision on pulmonary abnormalities. When the system is presented with a digital CXR image from the Picture Archive and Communication Systems (PACS) or an imaging source, it automatically identifies the lung regions in the image, extracts image features, and classifies the image as normal or abnormal using trained machine-learning algorithms. The system has been trained on adult CXR images, and this article presents enhancements toward including pediatric CXR images. Our adult lung boundary detection algorithm is model-based. We note the lung shape differences during pediatric developmental stages, and adulthood, and propose building new lung models suitable for pediatric developmental stages. In this study, we quantify changes in lung shape from infancy to adulthood toward enhancing our lung segmentation algorithm. Our initial findings suggest pediatric age groupings of 0 - 23 months, 2 - 10 years, and 11 - 18 years. We present justification for our groupings. We report on the quality of boundary detection algorithm with the pediatric lung models.
Detecting documents with a certain stamp instance is an effective and reliable way to retrieve documents associated with a specific source. However, this unique problem has essentially remained unaddressed. In this paper, we present a novel stamp detection framework based on parameter estimation of connected edge features. Using robust basic-shape detectors, the approach is effective for stamps with analytically shaped contours, when only limited samples are available. For elliptic/circular stamps, it efficiently exploits the orientation information from pairs of edge points to determine its center position and area, without computing all the five parameters of an ellipse. In our approach, we considered the set of unique characteristics of stamp patterns. Specifically, we introduced effective algorithms to address the problem that stamps often spatially overlay their background contents. These give our approach significant advantages in detection accuracy and computation complexity over traditional Hough transform method in locating candidate ellipse regions. Experimental results on real degraded documents demonstrated the robustness of this retrieval approach on large document database, which consists of both printed text and handwritten notes.
Most researchers would agree that research in the field of document processing can benefit tremendously from a common software library through which institutions are able to develop and share research-related software and applications across academic, business, and government domains. However, despite several attempts in the past, the research community still lacks a widely-accepted standard software library for document processing. This paper describes a new library called DOCLIB, which tries to overcome the drawbacks of earlier approaches. Many of DOCLIB's features are unique either in themselves or in their combination with others, e.g. the factory concept for support of different image types, the juxtaposition of image data and metadata, or the add-on mechanism. We cherish the hope that DOCLIB serves the needs of researchers better than previous approaches and will readily be accepted by a larger group of scientists.