A Mask Based Segmentation Algorithm for Automatic Measurement of Cobb Angle from Scoliosis X-Ray Image

2012 ◽  
Author(s):  
Binoshi Samuvel ◽  
Vinu Thomas ◽  
Mini M.G. ◽  
Renjith Kumar J.
Keyword(s):  
Cobb Angle ◽  
Automatic Measurement ◽  
Segmentation Algorithm ◽  
X Ray

Automatic measurement of SAED patterns from asbestos minerals

1988 ◽  
Vol 46 ◽  
pp. 846-847
Author(s):  
J. C. Russ ◽  
T. Taguchi ◽  
P. M. Peters ◽  
E. Chatfield ◽  
J. C. Russ ◽  
...  
Keyword(s):  
Diffraction Pattern ◽  
High Precision ◽  
Diffraction Data ◽  
Automatic Measurement ◽  
Automatic Method ◽  
Important Method ◽  
X Ray ◽  
Integrated Intensity ◽  
Asbestiform Minerals ◽  
True Center

Conventional SAD patterns as obtained in the TEM present difficulties for identification of materials such as asbestiform minerals, although diffraction data is considered to be an important method for making this purpose. The preferred orientation of the fibers and the spotty patterns that are obtained do not readily lend themselves to measurement of the integrated intensity values for each d-spacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. We have implemented an automatic method for diffraction pattern measurement to overcome these problems. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring. The resulting spectrum of intensity vs. radius is then used just as a conventional X-ray diffractometer scan would be, to locate peaks and produce a list of d,I values suitable for search/match comparison to known or expected phases.

Inter- and intra-observer reliability of the Cobb angle in the measurement of vertebral, local and segmental kyphosis of traumatic lumbar spine fractures in the lateral X-ray

2010 ◽  
Vol 130 (12) ◽  
pp. 1533-1538 ◽  
Author(s):  
Benjamin Ulmar ◽  
Markus Gühring ◽  
Traude Schmälzle ◽  
Kuno Weise ◽  
Andreas Badke ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Cobb Angle ◽  
Spine Fractures ◽  
X Ray ◽  
Observer Reliability

Automatic measurement of yarn hairiness based on the improved MRMRF segmentation algorithm

2017 ◽  
Vol 109 (6) ◽  
pp. 740-749 ◽  
Author(s):  
Junfeng Jing ◽  
Mengying Huang ◽  
Pengfei Li ◽  
Xiaocui Ning
Keyword(s):  
Automatic Measurement ◽  
Segmentation Algorithm ◽  
Yarn Hairiness

scholarly journals IN-BRACE CORRECTION (IBC) TERHADAP TIPE DAN BESARAN KURVA PADA PASIEN ADOLESCENT IDIOPATHIC SKOLIOSIS (AIS)

2020 ◽  
Vol 14 (1) ◽  
pp. 46-52
Author(s):  
Raden Candra ◽  
Fika Trifani
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
P Value ◽  
X Ray

Skoliosis adalah kelengkungan tulang belakang ke lateral yang melebihi 10 derajat. Tinjauan lapangan pada klinik dan rumah sakit di Indonesia menunjukan banyaknya kasus pasien Adolescent Idiopathic Scoliosis (AIS) yang telah ditangani dengan penggunaan skoliosis brace. In-brace correction (IBR) merupakan cara menilai kualitas skoliosis brace secara cepat setelah brace dipasangkan kepada pasien dengan metode X-Ray dengan menggunakan brace. Akan tetapi, hasil IBR tersebut sering ditemukan berbeda dari satu pasien dengan yang lainnya sehingga dibutuhkan untuk mengetahui faktor yang dapat menyebabkan perbedaan tersebut. Oleh karena itu, tujuan pada penelitian ini adalah untuk menilai apakah terdapat hubungan antara tipe kurva dan besaran kurva terhadap IBR pada pasien AIS. Analisis retrospective sebanyak 120 data sekunder telah digunakan dalam penelitian ini melalui rekam medis pasien yang menggunakan scoliosis brace dari tahun 2016 - 2018. Data yang diambil berupa Cobb angle tanpa menggunakan brace, In-Brace Cobb angle, dan tipe kurva skoliosis. Rata-rata IBR adalah 56,0% pada besaran kurva ringan (20°-29°), 37,2% pada besaran kurva sedang (30° - 40°), 36,7% pada besaran kurva parah (>40°). Sedangkan, rata-rata IBR tertinggi adalah pada tipe kurva ganda dimana lumbar > thoraks yaitu sebesar 50,3%, lalu disusul dengan kurva tunggal thoraks dan kurva ganda thoraks > lumbar sebesar 40,3% dan 39,1% secara berurutan. terdapat perbedaan yang signifikan IBR bedasarkan Besaran Kurva dan Tipe Kurva pada pasien adolescent idiopatik skoliosis dengan p value 0,000 dan 0,029 secara berurutan. Dapat disimpulkan bahwa tipe dan besaran kurva scoliosis merupakan faktor yang dapat mempengaruhi hasil IBR secara signifikan

scholarly journals Problems with diagnosing early stadia of scoliosis due to discrepancies between ATR and the Cobb angle

2020 ◽  
Author(s):  
Marek Kluszczyński ◽  
Jacek Wąsik ◽  
Dorota Ortenburger
Keyword(s):  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Lumbar Lordosis ◽  
Thoracic Kyphosis ◽  
Clinical Parameters ◽  
Trunk Rotation ◽  
X Ray ◽  
False Diagnosis ◽  
Cobb Method ◽  
Threshold Rule

Abstract Background This research analysed discrepancies between the angle of trunk rotation (ATR) and the Cobb angle, in order to study if the commonly used 7° cut-off threshold for ATR helps diagnose scoliosis. In early stadia of scoliosis in children, ATR and the Cobb angle often disagree, increasing the risk of a false diagnosis: while the former does not suggest scoliosis, the latter does. Methods The study analysed ATR clinical parameters and the Cobb angle in the X-ray pictures of 117 (23 boys and 94 girls, aged 6–17 years) children who had not yet started treatment and whose X-ray pictures showed the Cobb angle of at least 10°, indicating idiopathic scoliosis. The degrees of lumbar lordosis and thoracic kyphosis were measured using the Saunders inclinometer, and back asymmetry was measured with Adam’s forward bend test using the Bunnell scoliometer. In the X-ray pictures, the curvature angle was plotted according to the Cobb method. The patients were stratified based on their age, and their ATRs and Cobb angles were compared. Results Although all the children had the Cobb angle over 10°, in 69 out of 117 (59%), ATR was below 7%. So, using the 7° cut-off threshold rule, scoliosis would not be diagnosed in those children. This shows that the two tests often disagree, suggesting that the 7° cut-off threshold or ATR is ineffective in diagnosing scoliosis. Conclusions To improve the method for diagnosing scoliosis based on ATR, consideration should be given to lowering the 7° ATR cut-off threshold.

scholarly journals Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report

2020 ◽  
Vol 8 (3) ◽  
pp. 317-326
Author(s):  
Grigory A. Lein ◽  
Natalia S. Nechaeva ◽  
Gulnar М. Mammadova ◽  
Andrey A. Smirnov ◽  
Maxim M. Statsenko
Keyword(s):  
Neural Network ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
X Rays ◽  
X Ray ◽  
Scoliotic Deformity ◽  
Window Method ◽  
Treatment Type

Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type. Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tangent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instrument of automatic analysis of the spine X-rays in scoliosis patients. Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemented and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research. Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving 85% in training neural network to determine the Cobb angle. Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.

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