Ewa Pietka

Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction

Clinical assessment of skeletal maturity is based on a visual comparison of a left-hand wrist radiograph with atlas patterns. Using a new digital hand atlas an image analysis methodology is being developed. To assist radiologists in bone age estimation. The analysis starts with a preprocessing function yielding epiphyseal/metaphyseal regions of interest (EMROIs). Then, these regions are subjected to a feature extraction function. Accuracy has been measured independently at three stages of the image analysis: detection of phalangeal tip, extraction of the EMROIs, and location of diameters and lower edge of the EMROIs. Extracted features describe the stage of skeletal development more objectively than visual comparison.

Skeletal Age Determinations in Children of European and African Descent: Applicability of the Greulich and Pyle Standards

This study assesses the value of the Greulich and Pyle method in determining the skeletal ages of healthy American children of European and African descent born after the year 1980. The hand and wrist radiographs of 534 children (265 boys, 269 girls; 260 European-Americans [EA], 274 African-Americans [AA]), ages 0 to 19 y, were analyzed by two experienced pediatric radiologists blinded to the chronological age of the subjects. A difference score was calculated for each subject by subtracting chronological age from the mean bone ages scores provided by the two raters. One group t-tests were performed to verify the hypothesis that the mean difference score was equal to zero. Skeletal age determinations by the two radiologists showed a high degree of agreement by intraclass correlation coefficient (r = 0.994). The range of values for differences in skeletal and chronological ages was very wide, indicating great individual variability. Comparisons between skeletal and chronological age only reached statistical significance in EA prepubertal girls, whose skeletal ages were delayed, on average, by three months (t = −2.9;p = 0.005). Mean difference between skeletal and chronological age in prepubertal children of African descent was 0.09 ± 0.66 y, while that in children of European descent was −0.17 ± 0.67 y; (t = 3.13;p = 0.0019). On average, the bone ages of 10% of all prepubertal AA children were 2 SD above the normative data in the Greulich and Pyle atlas, while the bone ages of 8% of all prepubertal EA children were 2 SD below. In contrast to the racial differences observed in prepubertal children, EA postpubertal males had significantly greater values for bone age than AA postpubertal males (t = 2.03;p = 0.05). In conclusion, variations in skeletal maturation in prepubertal children are greater than those reflected in the Greulich and Pyle atlas; prepubertal American children of European descent have significantly delayed skeletal maturation when compared with those of African descent; and, postpubertal EA males have significantly advanced skeletal maturation when compared with postpubertal AA males. New standards are needed to make clinical decisions that require reliable bone ages and to accurately represent a multiethnic pediatric population.

Information Technologies in Biomedicine

As the medical information systems have been integrated in order to address the core of medicine, including patient care in ambulatory and in-patient setting, computer assisted diagnosis and treatment, telemedicine, and home care we are witnessing radical changes in the Information Technologies. This will continue in the years to come. This book presents a comprehensive study in this field and contains carefully selected articles contributed by experts of information technologies. It is an interdisciplinary collection of papers that have both a theoretical and applied dimension. In particular, it includes the following sections: - Image Processing and CAD, - Signal Processing, - Biotechnology, - Data Analysis, - Multimedia, - Biomechanics. This book is a great reference tool for scientists who deal with problems of designing and implementing information processing tools employed in systems that assist the clinicians in patient diagnosis and treatment.

Integration of computer assisted bone age assessment with clinical PACS

Computer assisted bone age assessment (BAA) integrated with a clinical PACS is described. The image analysis is performed on a DICOM compliant workstation able to accept images from a PACS server or directly from an image modality (digital radiography or film scanner). Images can be processed in two modes. If the image is acquired from a normally developed subject, it can be added to the digital hand atlas. An image may also be subjected only to a diagnostic analysis for the BAA without archiving the features in the database. The image analysis is performed in three steps. A location of six region of interest is followed by their segmentation and feature extraction. The features analysis results in retrieving the closest image match from the standard database. Based on currently analyzed image data in the hand atlas, the standard deviation of the assessment bone age does not exceed 1 yr of age.

Feature extraction in carpal-bone analysis

Hand-bone analysis with image processing techniques using a digital radiograph can be used to assess skeletal age. The analysis consists of two steps: phalangeal and carpal bone analysis. The carpal bone analysis is discussed. First, the carpal bone region of interest (CROI) is defined using a standard thresholding technique to separate the hand from the background. Then, a dynamic thresholding method with variable window sizes is used to differentiate between the bones and the soft tissue. Next, the radius, ulna, and metacarpals intersecting the borders of the CROI are removed by using mathematical morphology. Finally, all objects included in the corrected CROI are separated and described in terms of features. These features describe the size, shape, and location and include some gray-scale pixel value information. On the basis of this analysis, the separation of the noncarpal bone objects from the carpal bone is possible. The feature selection step removes features of low discriminant power and reduces the space dimension. The remaining carpal bone parameters are used for further analysis leading to skeletal age assessment.

Computer-assisted bone age assessment based on features automatically extracted from a hand radiograph

This paper presents a computer-aided classification algorithm to assist the radiologist in the bone age assessment of pediatric patients. The classification is based on features automatically extracted from two regions of Computed Radiography (CR) left hand wrist images: phalangeal region of interest (PROI) and carpal bone region of interest (CROI). Due to imprecise nature of the bone age assessment problem, a fuzzy classifier for both regions has been developed. After defining a membership function for each region, features are processed yielding a matrix which maps the set of features to a year of age within the predefined range. The grades of membership are described as membership function values in the interval [0, 1]. A classification rule based on a max-sum operator, processes the matrix assessing the bone age. Since both regions are analyzed independently, two bone age assessments are obtained. They reflect the phalangeal and carpal bones maturity individually. In pathological cases the discrepancy between both assessments may reach as much as 2 yr.

Lung segmentation in digital radiographs

Computer-assisted interpretation of computer radiography (CR) chest images including lung nodules detection, quantitative texture analysis, etc requires a lung delineation algorithm that restricts the area to be analyzed. This report presents a new lung-segmentation technique. It is performed in three phases. First, a histogram analysis finds a threshold value that eliminates the densest anatomic regions. Then, a gradient analysis separates the lungs from parts of thorax attached to the lungs that have not been removed in the previous phase. A smoothing routine yields the final image. By imposing a testing condition that results from the histogram analysis, underexposed images are not being considered. If being segmented, they exhibit a significant lung penetration. The test increases the accuracy of the procedure and makes it safer for an unsupervised application. The segmentation procedure has been implemented together with preprocessing functions in our clinical picture archiving and communication system.

Computer-Assisted Bone Age Assessment: Graphical User Interface for Image Processing and Comparison

The current study is part of a project resulting in a computer-assisted analysis of a hand radiograph yielding an assessment of skeletal maturity. The image analysis is based on features selected from six regions of interest. At various stages of skeletal development different image processing problems have to be addressed. At the early stage, feature extraction is based on Lee filtering followed by the random Gibbs fields and mathematical morphology. Once the fusion starts, wavelet decomposition methods are implemented. The user interface displays the closest neighbors to each image under consideration. Results show the sensitivity of different regions to both stages of development and certain feature sensitivity within each region. At the early stage of development, the distal features are more reliable indicators, whereas at the stage of epiphyseal fusion, a larger dynamic range of middle features makes them more sensitive. In the current study, a graphical user interface has been designed and implemented for testing the image processing routines and comparing the results of quantitative image analysis with the visual interpretation of extracted regions of interest. The user interface may also serve as a teaching tool. At the later stage of the project it will be used as a classification tool.

Computer Automated Approach to the Extraction of Epiphyseal Regions in Hand Radiographs

Epiphyseal region is the most sensitive region to developmental changes of the skeletal system. Extraction of this area is the very first step in any computerized image analysis. In this report a fully automated analysis of a hand radiograph resulting in extraction of distal and middle regions of the II, III, and IV phalanx is presented. The processing is performed in 3 stages. First, the trend of background is removed from radiograph to obtain a binary hand mask. At this stage a labeling procedure is necessary to eliminate artifacts (markers). Then, II, III, and IV phalanges are identified in the binary image, and the phalangeal axes are drawn. Finally, the intensity profile along each phalangeal axis is analyzed, and, on its basis, distal and middle regions are located. The presented procedure is designed as a part of currently developed system for automatic bone age assessment; however, it also can be as a preprocessing step in other diseases the diagnoses of which may require a computer assistance.

Soft computing approach to 3D lung nodule segmentation in CT

This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database.