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Computer-assisted bone age assessment—database adjustment
International Congress Series 1256 (2003) 87 – 92
Computer-assisted bone age assessment—database adjustment Ewa Pietka * Silesian University of Technology, Institute of Electronics, ul. Akademicka 16, PL-44 101 Gliwice, Poland Received 14 March 2003; received in revised form 14 March 2003; accepted 17 March 2003
Abstract Computer-added bone age assessment is presented as a two-module workstation able to store and adjust the data extracted from a radiograph of normally developed subjects as well as process a clinical image in order to assess the skeletal development. The database structure has been presented and the data access discussed. The database stores features extracted from a hand radiograph and data required to restore region of interest (ROI) for the evaluation of the image analysis. The module, which updates of the standard image data, is incorporated into the computer-added bone age assessment workstation. It permits a more objective assessment of skeletal maturity based on adjusted database to be performed. D 2003 Published by Elsevier Science B.V. Keywords: Computer-assisted diagnosis; Bone age assessment; Medical data
1. Introduction Bone age assessment is a procedure frequently performed in the paediatric radiology. Based on a radiological examination of a left hand wrist, the bone age is assessed and then compared with the chronological age. A discrepancy between these two values indicates abnormalities in skeletal development. This examination is universally used due to the advantage of simplicity, a minimum of radiation exposure, and the availability of multiple ossification centres for evaluation of maturity. It is an important procedure in the diagnosis and management of endocrine disorders serving subsequently as one index of therapeutic effect. Being a useful procedure in the diagnostic evaluation of metabolic and growth * Tel.: +48-609-833-512; fax: +48-32-237-2225. E-mail address: [email protected] (E. Pietka). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00295-4
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abnormalities, it indicates as well the acceleration or decrease of maturation in a variety of syndromes, malformations, and bone dysplasias. Two methods are commonly used in clinical diagnosis. The first one is the atlas matching method [1]. It is based on a left hand wrist radiograph comparison with the series of radiographs grouped in the atlas according to age and sex. The closest match identifies the skeletal age. The second method is based on a detailed analysis of hand bones [2]. Each complex is assigned to one of eight classes and described in terms of scores. The sum of all scores assesses the bone age. The goal of the current study is to briefly present a two-module workstation able to store and adjust the data extracted from a radiograph of normally developed subjects as well as process a clinical image in order to assess the skeletal development.
2. Methods and materials 2.1. Image analysis Bone age assessment is based on an analysis of ossification centres in epiphyses of tubular bones including distals and middles of the II, III, and IV phalanx. Epiphyses ossify usually after the birth. When the development progresses, the bony penetration advances from the initial focus in all directions. Penetration continues until the edges of metaphyses are reached. The gap between the shaft and the ossification centre diminishes progressively until it disappears, and the epiphysis and metaphysis fuse into one adult bone. The image analysis includes three phases [3]: image preprocessing, region of interest (ROI) extraction, and feature extraction. The analysis starts with the background subtraction, based on a histogram analysis. Then, the hand is extracted and subjected to a procedure that locates axes of the II, III, and IV phalanx. The selection of regions of interest is based on medical knowledge of the changes in anatomical structures during the developmental process. An overview of medically accepted diagnostic method indicates that epi –metaphyseal regions of interest appear to be the most sensitive areas reflecting the developmental stage. Thus, along the epiphyseal axes six regions of interest are extracted. For the image analysis, the process of skeletal developmental is divided into two phases: an early and later stage of development. At the early stage of skeletal development, the epiphyses are separated from the metaphyses. In the process of development, they change from a disc shape with concave borders to a full size when the epiphyses cap the metaphyses. In this phase, features describing the size and shape of epiphysis are of high discrimination power. In the later stage, the gap between epiphysis and metaphysis starts disappearing and fusion begins. It continues until fusion is completed and one adult bone is found. In this phase, the stage of fusion is estimated. Due to a very different appearance of epiphyses in each phase, various methods of image processing are employed. For the early stage of development, regions of interest are subjected to an image segmentation procedure that separates epiphyses from metaphyses [4]. Then, the features contain distance-related, area-related, and contrast-related parameters [5]. At the later stage of development, when the epiphyseal fusion has started, a
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wavelet analysis is used [6]. The wavelet decomposition procedure permits the energy of horizontal components and texture-related parameters to be extracted. Another group of features describes the directional information derived from the wavelet modulus and wavelet angle. Finally, a fuzzy inference system [7] has been developed to assess the skeletal age. 2.2. Database The analysis is performed on left hand wrist radiographs selected from a normal population and organised into four groups: black male and female and white male and female. Based on preliminary result, in prepubertal children (0 to 9-year-old), 5 images for each age group are collected, whereas in children during puberty (10- to 18-year-old), 10 images for each age group are collected. This gives 135 images per group and a total of 540 images. These images have been acquired at the Childrens Hospital Los Angeles/ USC. The database acquires three types of data. One results from the image analysis and contains patient demographics and features extracted from the radiograph. Separate structures are created for feature sets of the early and later stage of development. From a radiograph reflecting the early or late stage of development, only one set of features is extracted. Yet, there is a range of patients’ age which may yield both sets of findings. The second type of features extracts data which permits the region of interest to be viewed. Moreover, all markers (lines and edges) which serve as reference levels in feature extraction are shown. The coordinates and distances of found objects, archived in the database, permit their location on the displayed region of interest. The third type of data consists of images archived in a full diagnostic resolution as well as in a reference format. They are used for displaying the closest match with the diagnosed image. The database is accessed by the adjustment process and the computer-added bone age assessment (Fig. 1). The first case takes place when a radiograph is a hand image of a subject belonging to a control group. After the image analysis is performed, a detailed evaluation is performed by an authorised user. It includes several steps, starting with the image quality, particular the location of the hand wrist. Bound or rotated fingers may effect changes in the features values and lead to miscalculation of parameters of the classifier. Then, the location of all regions of interest and markers (Fig. 2) found during the image analysis is verified. Next, using the classification process [7], the bone age is assessed. Finally, a clinical verification is required. The closest images from the database matching the candidate image are displayed (Fig. 3) and based on the comparison a final acceptance is made. If the test is passed, extracted features, together with the patients data is stored in the data. The images are archived in full resolution and a compressed reference format. This performance makes the system ready for clinical tests and implementation. The second procedure is performed when a clinical patient image is to be diagnosed. Thus, the image is transferred and analysed at the bone age assessment workstation. The workstation is Digital Imaging and Communication in Medicine (DICOM)-compliant. Unlike at the adjustment procedure, the verification is possible, yet, not mandatory. The classification is performed and at this stage a medical verification is required. It is based on
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Fig. 1. Database management in bone age assessment.
the comparison of the clinical image with the closest image match found in the digital hand atlas. The image is transferred to the workstation in order to permit the verification. Verification is based on a comparison with the best match retrieved from the archive (Fig. 3). The evaluation finishes the procedure.
Fig. 2. Markers viewed at the region of interest.
E. Pietka / International Congress Series 1256 (2003) 87–92
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Fig. 3. Bone age assessment performance.
During the clinical performance, the database can be updated as well. In order to do it, an image has to be transferred to the database adjustment phase and pass all steps of evaluation. 2.3. Database management The data is stored in a SQL database. The SQL query retrieves also image data. The database management provides the integration of the image data, patient information, and radiological findings. New patient record may be added or existing records may be deleted or corrected. Relationship among medical records will permit a verification of bony structures. The database will be a source of information for a user graphical interface that displays the classification results or is able to view the data describing a particular stage of development. In order to introduce the data integrity, two types of data access are implemented: database adjustment and clinical implementation for diagnostic purpose. The database adjustment is split into two phases. It has to start with the database organisation at the developmental stage and then be updated during the adjustment sessions. Features extracted from normally developed hand radiographs are added to the database. Next, the database is used as a training set at the developmental stage of the classifier as well as a reference set of data at the medical diagnosis stage. Implementation of the system requires a data access permission to be defined for each user. The data access is defined at two levels of authorisation. Changes in the existing data as well as adjustment of the standard database (addition or removal of the images and relevant features) can be performed by the data administrator, whereas for diagnostic purpose, a read-only access is granted.
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3. Results Features extracted from hand images of normal population of boys (270 cases) and girls (270 cases) of two ethnic groups between 1 and 19 years of age have been stored in the database. The assessment of bone age has been tested. An average difference of evaluated bone age and chronological age error does not exceed 1 year of age. A higher difference in girls at the age of 16– 17 years is caused by an imprecise age assessment after the epiphyseal fusion in phalanxes is completed.
4. Conclusion The implementation of computers in the medical diagnosis improves the analysis in three different areas. Firstly, it increases the objectivity by using quantitative features instead of a visual interpolation. These features are extracted automatically rather than manually in order to accelerate the analysis and make the measurements independent from the external conditions (light), measurement inexactness, or errors (calibration errors, etc.). Secondly, a transparent algorithm is defined to standardise the decision-making process in the feature analysis. This again increases the objectivity and solves the problem of replicability. Finally, it permits a set of images and features describing them to be collected and recognised as a medical standard. The system is DICOM-compliant and thus can be integrated with Picture Archiving and Communication Systems and/or and digital radiography or scanner.
References [1] W.W. Greulich, S.I. Pyle, Radiographic Atlas of Skeletal Development of Hand Wrist, Stanford Univ. Press, Stanford, CA, 1959. [2] J.M. Tanner, R.H. Whitehouse, Assessment of Skeletal Maturity and Prediction of Adult Height (TW2) Method, Academic Press, London, 1975. [3] E. Pietka, A. Gertych, S. Pos´piech, F. Cao, H.K. Huang, V. Gilsanz, Computer assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction, IEEE Trans. Med. Imag. (1999) (wyslane do druku). [4] A. Gertych, E. Pietka, F. Cao, H.K. Huang, Computer assisted bone age assessment: region of interest segmentation, Symbiosis, Szczyrk, 2001, pp. 67 – 71. [5] A. Gertych, E. Pietka, An automated segmentation and features extraction from hand radiographs, ICCVG— International Conference on Computer Vision and Graphics, Zakopane, Poland, 2002, Sept. 26 – 29, in press. [6] S. Pospiech-Kurkowska, E. Pietka, F. Cao, H.K. Huang, Directional analysis in assessment of epiphyseal fusion, Symbiosis, 2001, pp. 61 – 66. [7] S. Pospiech-Kurkowska, E. Pietka, F. Cao, H.K. Huang, Fuzzy system for the estimation of the bone age from wavelet features, Proc. BIOSIGNAL, Brno, 2002, pp. 441 – 443. A
Computer-assisted bone age assessment—database adjustment Ewa Pietka * Silesian University of Technology, Institute of Electronics, ul. Akademicka 16, PL-44 101 Gliwice, Poland Received 14 March 2003; received in revised form 14 March 2003; accepted 17 March 2003
Abstract Computer-added bone age assessment is presented as a two-module workstation able to store and adjust the data extracted from a radiograph of normally developed subjects as well as process a clinical image in order to assess the skeletal development. The database structure has been presented and the data access discussed. The database stores features extracted from a hand radiograph and data required to restore region of interest (ROI) for the evaluation of the image analysis. The module, which updates of the standard image data, is incorporated into the computer-added bone age assessment workstation. It permits a more objective assessment of skeletal maturity based on adjusted database to be performed. D 2003 Published by Elsevier Science B.V. Keywords: Computer-assisted diagnosis; Bone age assessment; Medical data
1. Introduction Bone age assessment is a procedure frequently performed in the paediatric radiology. Based on a radiological examination of a left hand wrist, the bone age is assessed and then compared with the chronological age. A discrepancy between these two values indicates abnormalities in skeletal development. This examination is universally used due to the advantage of simplicity, a minimum of radiation exposure, and the availability of multiple ossification centres for evaluation of maturity. It is an important procedure in the diagnosis and management of endocrine disorders serving subsequently as one index of therapeutic effect. Being a useful procedure in the diagnostic evaluation of metabolic and growth * Tel.: +48-609-833-512; fax: +48-32-237-2225. E-mail address: [email protected] (E. Pietka). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00295-4
88
E. Pietka / International Congress Series 1256 (2003) 87–92
abnormalities, it indicates as well the acceleration or decrease of maturation in a variety of syndromes, malformations, and bone dysplasias. Two methods are commonly used in clinical diagnosis. The first one is the atlas matching method [1]. It is based on a left hand wrist radiograph comparison with the series of radiographs grouped in the atlas according to age and sex. The closest match identifies the skeletal age. The second method is based on a detailed analysis of hand bones [2]. Each complex is assigned to one of eight classes and described in terms of scores. The sum of all scores assesses the bone age. The goal of the current study is to briefly present a two-module workstation able to store and adjust the data extracted from a radiograph of normally developed subjects as well as process a clinical image in order to assess the skeletal development.
2. Methods and materials 2.1. Image analysis Bone age assessment is based on an analysis of ossification centres in epiphyses of tubular bones including distals and middles of the II, III, and IV phalanx. Epiphyses ossify usually after the birth. When the development progresses, the bony penetration advances from the initial focus in all directions. Penetration continues until the edges of metaphyses are reached. The gap between the shaft and the ossification centre diminishes progressively until it disappears, and the epiphysis and metaphysis fuse into one adult bone. The image analysis includes three phases [3]: image preprocessing, region of interest (ROI) extraction, and feature extraction. The analysis starts with the background subtraction, based on a histogram analysis. Then, the hand is extracted and subjected to a procedure that locates axes of the II, III, and IV phalanx. The selection of regions of interest is based on medical knowledge of the changes in anatomical structures during the developmental process. An overview of medically accepted diagnostic method indicates that epi –metaphyseal regions of interest appear to be the most sensitive areas reflecting the developmental stage. Thus, along the epiphyseal axes six regions of interest are extracted. For the image analysis, the process of skeletal developmental is divided into two phases: an early and later stage of development. At the early stage of skeletal development, the epiphyses are separated from the metaphyses. In the process of development, they change from a disc shape with concave borders to a full size when the epiphyses cap the metaphyses. In this phase, features describing the size and shape of epiphysis are of high discrimination power. In the later stage, the gap between epiphysis and metaphysis starts disappearing and fusion begins. It continues until fusion is completed and one adult bone is found. In this phase, the stage of fusion is estimated. Due to a very different appearance of epiphyses in each phase, various methods of image processing are employed. For the early stage of development, regions of interest are subjected to an image segmentation procedure that separates epiphyses from metaphyses [4]. Then, the features contain distance-related, area-related, and contrast-related parameters [5]. At the later stage of development, when the epiphyseal fusion has started, a
E. Pietka / International Congress Series 1256 (2003) 87–92
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wavelet analysis is used [6]. The wavelet decomposition procedure permits the energy of horizontal components and texture-related parameters to be extracted. Another group of features describes the directional information derived from the wavelet modulus and wavelet angle. Finally, a fuzzy inference system [7] has been developed to assess the skeletal age. 2.2. Database The analysis is performed on left hand wrist radiographs selected from a normal population and organised into four groups: black male and female and white male and female. Based on preliminary result, in prepubertal children (0 to 9-year-old), 5 images for each age group are collected, whereas in children during puberty (10- to 18-year-old), 10 images for each age group are collected. This gives 135 images per group and a total of 540 images. These images have been acquired at the Childrens Hospital Los Angeles/ USC. The database acquires three types of data. One results from the image analysis and contains patient demographics and features extracted from the radiograph. Separate structures are created for feature sets of the early and later stage of development. From a radiograph reflecting the early or late stage of development, only one set of features is extracted. Yet, there is a range of patients’ age which may yield both sets of findings. The second type of features extracts data which permits the region of interest to be viewed. Moreover, all markers (lines and edges) which serve as reference levels in feature extraction are shown. The coordinates and distances of found objects, archived in the database, permit their location on the displayed region of interest. The third type of data consists of images archived in a full diagnostic resolution as well as in a reference format. They are used for displaying the closest match with the diagnosed image. The database is accessed by the adjustment process and the computer-added bone age assessment (Fig. 1). The first case takes place when a radiograph is a hand image of a subject belonging to a control group. After the image analysis is performed, a detailed evaluation is performed by an authorised user. It includes several steps, starting with the image quality, particular the location of the hand wrist. Bound or rotated fingers may effect changes in the features values and lead to miscalculation of parameters of the classifier. Then, the location of all regions of interest and markers (Fig. 2) found during the image analysis is verified. Next, using the classification process [7], the bone age is assessed. Finally, a clinical verification is required. The closest images from the database matching the candidate image are displayed (Fig. 3) and based on the comparison a final acceptance is made. If the test is passed, extracted features, together with the patients data is stored in the data. The images are archived in full resolution and a compressed reference format. This performance makes the system ready for clinical tests and implementation. The second procedure is performed when a clinical patient image is to be diagnosed. Thus, the image is transferred and analysed at the bone age assessment workstation. The workstation is Digital Imaging and Communication in Medicine (DICOM)-compliant. Unlike at the adjustment procedure, the verification is possible, yet, not mandatory. The classification is performed and at this stage a medical verification is required. It is based on
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E. Pietka / International Congress Series 1256 (2003) 87–92
Fig. 1. Database management in bone age assessment.
the comparison of the clinical image with the closest image match found in the digital hand atlas. The image is transferred to the workstation in order to permit the verification. Verification is based on a comparison with the best match retrieved from the archive (Fig. 3). The evaluation finishes the procedure.
Fig. 2. Markers viewed at the region of interest.
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Fig. 3. Bone age assessment performance.
During the clinical performance, the database can be updated as well. In order to do it, an image has to be transferred to the database adjustment phase and pass all steps of evaluation. 2.3. Database management The data is stored in a SQL database. The SQL query retrieves also image data. The database management provides the integration of the image data, patient information, and radiological findings. New patient record may be added or existing records may be deleted or corrected. Relationship among medical records will permit a verification of bony structures. The database will be a source of information for a user graphical interface that displays the classification results or is able to view the data describing a particular stage of development. In order to introduce the data integrity, two types of data access are implemented: database adjustment and clinical implementation for diagnostic purpose. The database adjustment is split into two phases. It has to start with the database organisation at the developmental stage and then be updated during the adjustment sessions. Features extracted from normally developed hand radiographs are added to the database. Next, the database is used as a training set at the developmental stage of the classifier as well as a reference set of data at the medical diagnosis stage. Implementation of the system requires a data access permission to be defined for each user. The data access is defined at two levels of authorisation. Changes in the existing data as well as adjustment of the standard database (addition or removal of the images and relevant features) can be performed by the data administrator, whereas for diagnostic purpose, a read-only access is granted.
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E. Pietka / International Congress Series 1256 (2003) 87–92
3. Results Features extracted from hand images of normal population of boys (270 cases) and girls (270 cases) of two ethnic groups between 1 and 19 years of age have been stored in the database. The assessment of bone age has been tested. An average difference of evaluated bone age and chronological age error does not exceed 1 year of age. A higher difference in girls at the age of 16– 17 years is caused by an imprecise age assessment after the epiphyseal fusion in phalanxes is completed.
4. Conclusion The implementation of computers in the medical diagnosis improves the analysis in three different areas. Firstly, it increases the objectivity by using quantitative features instead of a visual interpolation. These features are extracted automatically rather than manually in order to accelerate the analysis and make the measurements independent from the external conditions (light), measurement inexactness, or errors (calibration errors, etc.). Secondly, a transparent algorithm is defined to standardise the decision-making process in the feature analysis. This again increases the objectivity and solves the problem of replicability. Finally, it permits a set of images and features describing them to be collected and recognised as a medical standard. The system is DICOM-compliant and thus can be integrated with Picture Archiving and Communication Systems and/or and digital radiography or scanner.
References [1] W.W. Greulich, S.I. Pyle, Radiographic Atlas of Skeletal Development of Hand Wrist, Stanford Univ. Press, Stanford, CA, 1959. [2] J.M. Tanner, R.H. Whitehouse, Assessment of Skeletal Maturity and Prediction of Adult Height (TW2) Method, Academic Press, London, 1975. [3] E. Pietka, A. Gertych, S. Pos´piech, F. Cao, H.K. Huang, V. Gilsanz, Computer assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction, IEEE Trans. Med. Imag. (1999) (wyslane do druku). [4] A. Gertych, E. Pietka, F. Cao, H.K. Huang, Computer assisted bone age assessment: region of interest segmentation, Symbiosis, Szczyrk, 2001, pp. 67 – 71. [5] A. Gertych, E. Pietka, An automated segmentation and features extraction from hand radiographs, ICCVG— International Conference on Computer Vision and Graphics, Zakopane, Poland, 2002, Sept. 26 – 29, in press. [6] S. Pospiech-Kurkowska, E. Pietka, F. Cao, H.K. Huang, Directional analysis in assessment of epiphyseal fusion, Symbiosis, 2001, pp. 61 – 66. [7] S. Pospiech-Kurkowska, E. Pietka, F. Cao, H.K. Huang, Fuzzy system for the estimation of the bone age from wavelet features, Proc. BIOSIGNAL, Brno, 2002, pp. 441 – 443. A