Determination of spinal curvature from scoliosis X-ray images using K-means and curve fitting for early detection of scoliosis disease
01 Nov 2017-
TL;DR: This research proposes an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated.
Abstract: One of the disease that require X-ray diagnosis is scoliosis Early detection of scoliosis is important to do for anyone From the early detection information, the doctor may take the firts step to further treatment quickly Determination of spinal curvature is a first step method that used to measure how severe the degree of scoliosis The severity degree of scoliosis can be assess by using Cobb angle Therefore, by approximate the spinal curvature, we can approximate the cobb angle too From previous work that interobserver measurement value may reach 118° and intraobserver measurement error is 6° So, as far as the cobb angle measuring, the subjectivity aspect is the natural thing and can be tolerated until now This research propose an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated The preprocessing has been done by canny edge detection The k-means clustering algorithm can detect the centroid point after segmentation preprocessing of the spinal segment and polynomial curve fitting will be used in the process for determining the spinal curve From the spinal curvature information, the scoliosis curve can be classified into 4 condition, normal, mild, moderate, and severe scoliosis
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TL;DR: The methods that will help clinicians to grade the severity of the disease with confidence are presented, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases.
Abstract: The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.
16 citations
TL;DR: The research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning by comparing the measurement effects of typical methods and their advantages and disadvantages.
Abstract: Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.
6 citations
TL;DR: In this paper, the authors explored the behavior of customer based marketing, through RFM Method (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.
Abstract: It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.
6 citations
28 Jul 2020
TL;DR: An end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis.
Abstract: Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.
4 citations
Cites background or result from "Determination of spinal curvature f..."
...fitting approach for Cobb angle measurement that requires a set of pre-processing steps [13]....
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...Comparing with some of the existing Cobb angle measurement techniques, our method achieves lower measurement error than those reported in [13] and [11]....
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01 Oct 2019
TL;DR: The proposed method, Bi-Histogram Equalization with adaptive sigmoid functions (BEASF), is a technique used for enhancing the spinal and vertebral bodies, and Density-based and Ellipse-like techniques are combined to locate the curve of the spine.
Abstract: X-ray images of the lateral spine are important for diagnosing spine problems such as osteoporosis, bone fractures, and spondylosis. In order to identify bone diseases, often a series of images is required. These are taken using a low level of X-ray radiation to reduce the risk of exposure to overshoot radiation. Dual Energy X-ray Absorptiometry is a standard medical tool used to diagnose bone diseases. In addition, the spine alignment of each individual person is different others. Therefore, developing an approach that can identify the spine area is challenging. In this work, the algorithm for automatic identification of spine and vertebral bodies is proposed. The proposed method consists of three main steps. The first step, Bi-Histogram Equalization with adaptive sigmoid functions (BEASF), is a technique used for enhancing the spinal and vertebral bodies. In the second step, Density-based and Ellipse-like techniques are combined to locate the curve of the spine. For the third step, object improvement techniques are applied to predict the location of vertebral bodies. The experimental results show that the approach reached 79.67% of Area Overlap Ratio. 81.67% of the Precision value.
2 citations
Cites methods from "Determination of spinal curvature f..."
...Bagus Adhi Kusuma [4] proposed an algorithm to automatically define spinal curvature from digital Xray images....
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References
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
"Determination of spinal curvature f..." refers methods in this paper
...The image that has been in the median filter then uses Canny edge detection [17] for finding the spine texture, as shown in Fig....
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01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
24,320 citations
"Determination of spinal curvature f..." refers methods in this paper
...Determination of the centroid using the K-means clustering was first proposed by Macqueen [19]....
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TL;DR: To quantitate the intrinsic error in measurement, fifty anteroposterior radiographs of patients who had scoliosis were each measured on six separate occasions by four orthopaedic surgeons using the Cobb method.
Abstract: To quantitate the intrinsic error in measurement, fifty anteroposterior radiographs of patients who had scoliosis were each measured on six separate occasions by four orthopaedic surgeons using the Cobb method For the first two measurements (Set I), each observer selected the end-vertebrae of the curve; for the next two measurements (Set II), the end-vertebrae were pre-selected and constant The last two measurements (Set III) were obtained in the same manner as Set II, except that each examiner used the same protractor rather than the one that he carried with him The pooled results of all four observers suggested that the 95 per cent confidence limit for intraobserver variability was 49 degrees for Set I, 38 degrees for Set II, and 28 degrees for Set III The interobserver variability was 72 degrees for Set I and 63 degrees for Sets II and III The mean angles differed significantly between observers, but the difference was smaller when the observers used the same protractor
540 citations
"Determination of spinal curvature f..." refers background in this paper
...Typically, changes 5o or more between the two periods of X-ray observation may indicate rising curve [10]....
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01 Jan 2006
TL;DR: A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis based on surface topographic images of human backs and outperformed a linear discriminant function classifier and a decision tree classifier on the dataset.
Abstract: A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69-85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset
107 citations
"Determination of spinal curvature f..." refers background in this paper
...Scoliosis is a three-dimensional structural abnormalities of the spine that is usually found of 2% - 4% of the population in adolescence, and 70% - 80% the cause of scoliosis is not known with certainty [1]....
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TL;DR: A computerized method automatically measured the Cobb angle on spinal posteroanterior radiographs to reduce variability of Cobb angle measurement for scoliosis assessment and showed high agreement between automatic and manual measurements.
Abstract: To reduce variability of Cobb angle measurement for scoliosis assessment, a computerized method was developed. This method automatically measured the Cobb angle on spinal posteroanterior radiographs after the brightness and the contrast of the image were adjusted, and the top and bottom of the vertebrae were selected. The automated process started with the edge detection of the vertebra by Canny edge detector. After that, the fuzzy Hough transform was used to find line structures in the vertebral edge images. The lines that fitted to the endplates of vertebrae were identified by selecting peaks in Hough space under the vertebral shape constraints. The Cobb angle was then calculated according to the directions of these lines. A total of 76 radiographs were respectively analyzed by an experienced surgeon using the manual measurement method and by two examiners using the proposed method twice. Intraclass correlation coefficients (ICC) showed high agreement between automatic and manual measurements (ICCs > 0.95). The mean absolute differences between automatic and manual measurements were less than 5 degrees . In the interobserver analyses, ICCs were higher than 0.95, and mean absolute differences were less than 5 degrees . In the intraobserver analyses, ICCs were 0.985 and 0.978, respectively, for each examiner, and mean absolute differences were less than 3 degrees . These results demonstrated the validity and reliability of the proposed method.
69 citations
"Determination of spinal curvature f..." refers background in this paper
...If you have severe scoliosis with Cobb curve above 45o, then the doctor will recommend surgery [9]....
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